Summary Most databases are designed to work with textual data, with some special purpose engines that support domain specific formats. TileDB is a data engine that was built to support every type of data by using multi-dimensional arrays as the foundational primitive. In this episode the creator and founder of TileDB shares how he first started working on the underlying technology and the benefits of using a single engine for efficiently storing and querying any form of data. He also discusses the shifts in database architectures from vertically integrated monoliths to separately deployed layers, and the approach he is taking with TileDB cloud to embed the authorization into the storage engine, while providing a flexible interface for compute. This was a great conversation about a different approach to database architecture and how that enables a more flexible way to store and interact with data to power better data sharing and new opportunities for blending specialized domains. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Stavros Papadopoulos about TileDB, the universal storage engine Interview Introduction How did you get involved in the area of data management? Can you start by describing what TileDB is and the problem that you are trying to solve with it? What was your motivation for building it? What are the main use cases or problem domains that you are trying to solve for? What are the shortcomings of existing approaches to database design that prevent them from being useful for these applications? What are the benefits of using matrices for data processing and domain modeling? What are the challenges that you have faced in storing and processing sparse matrices efficiently? How does the usage of matrices as the foundational primitive affect the way that users should think about data modeling? What are the benefits of unbundling the storage engine from the processing layer Can you describe how TileDB embedded is architected? How has the design evolved since you first began working on it? What is your approach to integrating with the broader ecosystem of data storage and processing utilities? What does the workflow look like for someone using TileDB? What is required to deploy TileDB in a production context? How is the built in data versioning implemented? What is the user experience for interacting with different versions of datasets? How do you manage the lifecycle of versioned data to allow garbage collection? How are you managing the governance and ongoing sustainability of the open source project, and the commercial offerings that you are building on top of it? What are the most interesting, unexpected, or innovative ways that you have seen TileDB used? What have you found to be the most interesting, unexpected, or challenging aspects of building TileDB? What features or capabilities are you consciously deciding not to implement? When is TileDB the wrong choice? What do you have planned for the future of TileDB? Contact Info LinkedIn stavrospapadopoulos on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links TileDB GitHub Data Frames TileDB Cloud MIT Intel Sparse Linear Algebra Sparse Matrices HDF5 Dask Spark MariaDB PrestoDB GDAL PDAL Turing Complete Clustered Index Parquet File Format Podcast Episode Serializability Delta Lake Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Event based data is a rich source of information for analytics, unless none of the event structures are consistent. The team at Iteratively are building a platform to manage the end to end flow of collaboration around what events are needed, how to structure the attributes, and how they are captured. In this episode founders Patrick Thompson and Ondrej Hrebicek discuss the problems that they have experienced as a result of inconsistent event schemas, how the Iteratively platform integrates the definition, development, and delivery of event data, and the benefits of elevating the visibility of event data for improving the effectiveness of the resulting analytics. If you are struggling with inconsistent implementations of event data collection, lack of clarity on what attributes are needed, and how it is being used then this is definitely a conversation worth following. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! If you’ve been exploring scalable, cost-effective and secure ways to collect and route data across your organization, RudderStack is the only solution that helps you turn your own warehouse into a state of the art customer data platform. Their mission is to empower data engineers to fully own their customer data infrastructure and easily push value to other parts of the organization, like marketing and product management. With their open-source foundation, fixed pricing, and unlimited volume, they are enterprise ready, but accessible to everyone. Go to dataengineeringpodcast.com/rudder to request a demo and get one free month of access to the hosted platform along with a free t-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Patrick Thompson and Ondrej Hrebicek about Iteratively, a platform for enforcing consistent schemas for your event data Interview Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Iteratively and your motivation for creating it? What are some of the ways that you have seen inconsistent message structures cause problems? What are some of the common anti-patterns that you have seen for managing the structure of event messages? What are the benefits that Iteratively provides for the different roles in an organization? Can you describe the workflow for a team using Iteratively? How is the Iteratively platform architected? How has the design changed or evolved since you first began working on it? What are the difficulties that you have faced in building integrations for the Iteratively workflow? How is schema evolution handled throughout the lifecycle of an event? What are the challenges that engineers face in building effective integration tests for their event schemas? What has been your biggest challenge in messaging for your platform and educating potential users of its benefits? What are some of the most interesting or unexpected ways that you have seen Iteratively used? What are some of the most interesting, unexpected, or challenging lessons that you have learned while building Iteratively? When is Iteratively the wrong choice? What do you have planned for the future of Iteratively? Contact Info Patrick LinkedIn @Patrickt010 on Twitter Website Ondrej LinkedIn @ondrej421 on Twitter ondrej on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Iteratively Syncplicity Locally Optimistic DBT Podcast Episode Snowplow Analytics Podcast Episode JSON Schema Master Data Management Podcast Episode SDLC == Software Development Life Cycle Amplitude Mixpanel Mode Analytics CRUD == Create, Read, Update, Delete Segment Podcast Episode Schemaver (JSON Schema Versioning Strategy) Great Expectations Podcast.init Interview Data Engineering Podcast Interview Confluence Notion Confluent Schema Registry Podcast Episode Snowplow Iglu Schema Registry Pulsar Schema Registry The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Finding connections between data and the entities that they represent is a complex problem. Graph data models and the applications built on top of them are perfect for representing relationships and finding emergent structures in your information. In this episode Denise Gosnell and Matthias Broecheler discuss their recent book, the Practitioner’s Guide To Graph Data, including the fundamental principles that you need to know about graph structures, the current state of graph support in database engines, tooling, and query languages, as well as useful tips on potential pitfalls when putting them into production. This was an informative and enlightening conversation with two experts on graph data applications that will help you start on the right track in your own projects. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Denise Gosnell and Matthias Broecheler about the recently published practitioner’s guide to graph data Interview Introduction How did you get involved in the area of data management? Can you start by explaining what your goals are for the Practitioner’s Guide To Graph Data? What was your motivation for writing a book to address this topic? What do you see as the driving force behind the growing popularity of graph technologies in recent years? What are some of the common use cases/applications of graph data and graph traversal algorithms? What are the core elements of graph thinking that data teams need to be aware of to be effective in identifying those cases in their existing systems? What are the fundamental principles of graph technologies that data engineers should be familiar with? What are the core modeling principles that they need to know for designing schemas in a graph database? Beyond databases, what are some of the other components of the data stack that can or should handle graphs natively? Do you typically use a graph database as the primary or complementary data store? What are some of the common challenges that you see when bringing graph applications to production? What have you found to be some of the common points of confusion or error prone aspects of implementing and maintaining graph oriented applications? When it comes to the specific technologies of different graph databases, what are some of the edge cases/variances in the interfaces or modeling capabilities that they present? How does the variation in query languages impact the overall adoption of these technologies? What are your thoughts on the recent standardization of GSQL as an ANSI specification? What are some of the scaling challenges that exist for graph data engines? What are the ongoing developments/improvements/trends in graph technology that you are most excited about? What are some of the shortcomings in existing technology/ecosystem for graph applications that you would like to see addressed? What are some of the cases where a graph is the wrong abstraction for a data project? What are some of the other resources that you recommend for anyone who wants to learn more about the various aspects of graph data? Contact Info Denise LinkedIn @DeniseKGosnell on Twitter Matthias LinkedIn @MBroecheler on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links The Practitioner’s Guide To Graph Data Datastax Titan graph database Goethe Graph Database NoSQL Relational Database Elasticsearch Podcast Episode Associative Array Data Structure RDF Triple Datastax Multi-model Graph Database Semantic Web Gremlin Graph Query Language Super Node Neuromorphic Computing Datastax Desktop The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary A majority of the scalable data processing platforms that we rely on are built as distributed systems. This brings with it a vast number of subtle ways that errors can creep in. Kyle Kingsbury created the Jepsen framework for testing the guarantees of distributed data processing systems and identifying when and why they break. In this episode he shares his approach to testing complex systems, the common challenges that are faced by engineers who build them, and why it is important to understand their limitations. This was a great look at some of the underlying principles that power your mission critical workloads. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! If you’ve been exploring scalable, cost-effective and secure ways to collect and route data across your organization, RudderStack is the only solution that helps you turn your own warehouse into a state of the art customer data platform. Their mission is to empower data engineers to fully own their customer data infrastructure and easily push value to other parts of the organization, like marketing and product management. With their open-source foundation, fixed pricing, and unlimited volume, they are enterprise ready, but accessible to everyone. Go to dataengineeringpodcast.com/rudder to request a demo and get one free month of access to the hosted platform along with a free t-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Kyle Kingsbury about his work on the Jepsen testing framework and the failure modes of distributed systems Interview Introduction How did you get involved in the area of data management? Can you start by describing what the Jepsen project is? What was your inspiration for starting the project? What other methods are available for evaluating and stress testing distributed systems? What are some of the common misconceptions or misunderstanding of distributed systems guarantees and how they impact real world usage of things like databases? How do you approach the design of a test suite for a new distributed system? What is your heuristic for determining the completeness of your test suite? What are some of the common challenges of setting up a representative deployment for testing? Can you walk through the workflow of setting up, running, and evaluating the output of a Jepsen test? How is Jepsen implemented? How has the design evolved since you first began working on it? What are the pros and cons of using Clojure for building Jepsen? If you were to start over today on the Jepsen framework what would you do differently? What are some of the most common failure modes that you have identified in the platforms that you have tested? What have you found to be the most difficult to resolve distributed systems bugs? What are some of the interesting developments in distributed systems design that you are keeping an eye on? How do you perceive the impact that Jepsen has had on modern distributed systems products? What have you found to be the most interesting, unexpected, or challenging lessons learned while building Jepsen and evaluating mission critical systems? What do you have planned for the future of the Jepsen framework? Contact Info aphyr on GitHub Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Jepsen Riak Distributed Systems TLA+ Coq Isabelle Cassandra DTest FoundationDB Podcast Episode CRDT == Conflict-free Replicated Data-type Podcast Episode Riemann Clojure JVM == Java Virtual Machine Kotlin Haskell Scala Groovy TiDB YugabyteDB Podcast Episode CockroachDB Podcast Episode Raft consensus algorithm Paxos Leslie Lamport Calvin FaunaDB Podcast Episode Heidi Howard CALM Conjecture Causal Consistency Hillel Wayne Christopher Meiklejohn Distsys Class Distributed Systems For Fun And Profit by Mikito Takada Christopher Meiklejohn Reading List The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Wind energy is an important component of an ecologically friendly power system, but there are a number of variables that can affect the overall efficiency of the turbines. Michael Tegtmeier founded Turbit Systems to help operators of wind farms identify and correct problems that contribute to suboptimal power outputs. In this episode he shares the story of how he got started working with wind energy, the system that he has built to collect data from the individual turbines, and how he is using machine learning to provide valuable insights to produce higher energy outputs. This was a great conversation about using data to improve the way the world works. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Michael Tegtmeier about Turbit, a machine learning powered platform for performance monitoring of wind farms Interview Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Turbit and your motivation for creating the business? What are the most problematic factors that contribute to low performance in power generation with wind turbines? What is the current state of the art for accessing and analyzing data for wind farms? What information are you able to gather from the SCADA systems in the turbine? How uniform is the availability and formatting of data from different manufacturers? How are you handling data collection for the individual turbines? How much information are you processing at the point of collection vs. sending to a centralized data store? Can you describe the system architecture of Turbit and the lifecycle of turbine data as it propagates from collection to analysis? How do you incorporate domain knowledge into the identification of useful data and how it is used in the resultant models? What are some of the most challenging aspects of building an analytics product for the wind energy sector? What have you found to be the most interesting, unexpected, or challenging aspects of building and growing Turbit? What do you have planned for the future of the technology and business? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Turbit Systems LIDAR Pulse Shaping Wind Turbine SCADA Genetic Algorithm Bremen Germany Pitch Yaw Nacelle Anemometer Neural Network Swarm64 Podcast Episode Tensorflow The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The first stage of every data pipeline is extracting the information from source systems. There are a number of platforms for managing data integration, but there is a notable lack of a robust and easy to use open source option. The Meltano project is aiming to provide a solution to that situation. In this episode, project lead Douwe Maan shares the history of how Meltano got started, the motivation for the recent shift in focus, and how it is implemented. The Singer ecosystem has laid the groundwork for a great option to empower teams of all sizes to unlock the value of their Data and Meltano is building the reamining structure to make it a fully featured contender for proprietary systems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Douwe Maan about Meltano, an open source platform for building, running & orchestrating ELT pipelines. Interview Introduction How did you get involved in the area of data management? Can you start by describing what Meltano is and the story behind it? Who is the target audience? How does the focus on small or early stage organizations constrain the architectural decisions that go into Meltano? What have you found to be the complexities in trying to encapsulate the entirety of the data lifecycle in a single tool or platform? What are the most painful transitions in that lifecycle and how does that pain manifest? How and why has the focus of the project shifted from its original vision? With your current focus on the data integration/data transfer stage of the lifecycle, what are you seeing as the biggest barriers to entry with the current ecosystem? What are the main elements of your strategy to address these barriers? How is the Meltano platform in its current incarnation implemented? How much of the original architecture have you been able to retain, and how have you evolved it to align with your new direction? What have you found to be the challenges that your users face when going from the easy on-ramp of local execution to then trying to scale and customize their pipelines for production use? What are the most critical features that you are focusing on building now to make Meltano competitive with managed platforms? What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Meltano? When is Meltano the wrong choice? What is your broad vision for the future of Meltano? What are the most immediate needs for contribution that will help you realize that vision? Contact Info Website DouweM on GitLab DouweM on GitHub @DouweM on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Meltano GitLab Mexico City Netherlands Locally Optimistic Singer Stitch Data DBT ELT Informatica Version Control Code Review CI/CD Jupyter Notebook LookML Meltano Modeling Syntax Redash Metabase Apache Superset Apache Airflow Luigi Prefect Dagster Transferwise Pipelinewise 12 Factor Application The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary There are an increasing number of use cases for real time data, and the systems to power them are becoming more mature. Once you have a streaming platform up and running you need a way to keep an eye on it, including observability, discovery, and governance of your data. That’s what the Lenses.io DataOps platform is built for. In this episode CTO Andrew Stevenson discusses the challenges that arise from building decoupled systems, the benefits of using SQL as the common interface for your data, and the metrics that need to be tracked to keep the overall system healthy. Observability and governance of streaming data requires a different approach than batch oriented workflows, and this episode does an excellent job of outlining the complexities involved and how to address them. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Andrew Stevenson about Lenses.io, a platform to provide real-time data operations for engineers Interview Introduction How did you get involved in the area of data management? Can you start by describing what Lenses is and the story behind it? What is your working definition for what constitutes DataOps? How does the Lenses platform support the cross-cutting concerns that arise when trying to bridge the different roles in an organization to deliver value with data? What are the typical barriers to collaboration, and how does Lenses help with that? Many different systems provide a SQL interface to streaming data on various substrates. What was your reason for building your own SQL engine and what is unique about it? What are the main challenges that you see engineers facing when working with streaming systems? What have you found to be the most notable evolutions in the community and ecosystem around Kafka and streaming platforms? One of the interesting features in the recent release is support for topologies to map out the relations between different producers and consumers across a stream. Why is that a difficult problem and how have you approached it? On the point of monitoring, what are the foundational challenges that engineers run into when trying to gain visibility into streams of data? What are some useful strategies for collecting and analyzing traces of data flows? As with many things in the space of data, local development and pre-production testing and validation are complicated due to the potential scale and variability of a production system. What advice do you have for engineers who are trying to establish a sustainable workflow for streaming applications? How do you facilitate the CI/CD process for enabling a culture of testing and establishing confidence in the correct functionality of your systems? How is the Lenses platform implemented and how has its design evolved since you first began working on it? What are some of the specifics of Kafka that you have had to reconsider or redesign as you began adding support for additional streaming engines (e.g. Redis and Pulsar)? What are some of the most interesting, unexpected, or innovative ways that you have seen the Lenses platform used? What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with Lenses? When is Lenses the wrong choice? What do you have planned for the future of the platform? Contact Info LinkedIn @StevensonA_D on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Lenses.io Babylon Health DevOps DataOps GitOps Apache Calcite kSQL Kafka Connect Query Language Apache Flink Podcast Episode Apache Spark Podcast Episode Apache Pulsar Podcast Episode StreamNative Episode Playtika Riskfuel(?) JMX Metrics Amazon MSK (Managed Streaming for Kafka) Prometheus Canary Deployment Kafka on Pulsar Data Catalog Data Mesh Podcast Episode Dagster Airflow The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary We have machines that can listen to and process human speech in a variety of languages, but dealing with unstructured sounds in our environment is a much greater challenge. The team at Audio Analytic are working to impart a sense of hearing to our myriad devices with their sound recognition technology. In this episode Dr. Chris Mitchell and Dr. Thomas le Cornu describe the challenges that they are faced with in the collection and labelling of high quality data to make this possible, including the lack of a publicly available collection of audio samples to work from, the need for custom metadata throughout the processing pipeline, and the need for customized data processing tools for working with sound data. This was a great conversation about the complexities of working in a niche domain of data analysis and how to build a pipeline of high quality data from collection to analysis. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Dr. Chris Mitchell and Dr. Thomas le Cornu about Audio Analytic, a company that is building sound recognition technology that is giving machines a sense of hearing beyond speech and music Interview Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Audio Analytic? What was your motivation for building an AI platform for sound recognition? What are some of the ways that your platform is being used? What are the unique challenges that you have faced in working with arbitrary sound data? How do you handle the collection and labelling of the source data that you rely on for building your models? Beyond just collection and storage, what is your process for defining a taxonomy of the audio data that you are working with? How has the taxonomy had to evolve, and what assumptions have had to change, as you progressed in building the data set and the resulting models? challenges of building an embeddable AI model update cycle difficulty of identifying relevant audio and dealing with literal noise in the input data rights and ownership challenges in collection of source data What was your design process for constructing a pipeline for the audio data that you need to process? Can you describe how your overall data management system is architected? How has that architecture evolved since you first began building and using it? A majority of data tools are oriented around, and optimized for, collection and processing of textual data. How much off-the-shelf technology have you been able to use for working with audio? What are some of the assumptions that you made at the start which have been shown to be inaccurate or in need of reconsidering? How do you address variability in the duration of source samples in the processing pipeline? How much of an issue do you face as a result of the variable quality of microphones in the embedded devices where the model is being run? What are the limitations of the model in dealng with complex and layered audio environments? How has the testing and evaluation of your model fed back into your strategies for collecting source data? What are some of the weirdest or most unusual sounds that you have worked with? What have been the most interesting, unexpected, or challenging lessons that you have learned in the process of building the technology and business of Audio Analytic? What do you have planned for the future of the company? Contact Info Chris LinkedIn Thomas LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Audio Analytic Twitter Anechoic Chamber EXIF Data ID3 Tags Polyphonic Sound Detection Score GitHub Repository ICASSP CES MO+ ARM Processor Context Systems Blog Post The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The majority of analytics platforms are focused on use internal to an organization by business stakeholders. As the availability of data increases and overall literacy in how to interpret it and take action improves there is a growing need to bring business intelligence use cases to a broader audience. GoodData is a platform focused on simplifying the work of bringing data to employees and end users. In this episode Sheila Jung and Philip Farr discuss how the GoodData platform is being used, how it is architected to provide scalable and performant analytics, and how it integrates into customer’s data platforms. This was an interesting conversation about a different approach to business intelligence and the importance of expanded access to data. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! GoodData is revolutionizing the way in which companies provide analytics to their customers and partners. Start now with GoodData Free that makes our self-service analytics platform available to you at no cost. Register today at dataengineeringpodcast.com/gooddata Your host is Tobias Macey and today I’m interviewing Sheila Jung and Philip Farr about how GoodData is building a platform that lets you share your analytics outside the boundaries of your organization Interview Introduction How did you get involved in the area of data management? Can you start by describing what you are building at GoodData and some of its origin story? The business intelligence market has been around for decades now and there are dozens of options with different areas of focus. What are the factors that might motivate me to choose GoodData over the other contenders in the space? What are the use cases and industries that you focus on supporting with GoodData? How has the market of business intelligence tools evolved in recent years? What are the contributing trends in technology and business use cases that are driving that change? What are some of the ways that your customers are embedding analytics into their own products? What are the differences in processing and serving capabilities between an internally used business intelligence tool, and one that is used for embedding into externally used systems? What unique challenges are posed by the embedded analytics use case? How do you approach topics such as security, access control, and latency in a multitenant analytics platform? What guidelines have you found to be most useful when addressing the concerns of accuracy and interpretability of the data being presented? How is the GoodData platform architected? What are the complexities that you have had to design around in order to provide performant access to your customers’ data sources in an interactive use case? What are the off-the-shelf components that you have been able to integrate into the platform, and what are the driving factors for solutions that have been built specifically for the GoodData use case? What is the process for your users to integrate GoodData into their existing data platform? What is the workflow for someone building a data product in GoodData? How does GoodData manage the lifecycle of the data that your customers are presenting to their end users? How does GoodData integrate into the customer development lifecycle? What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with GoodData? Can you give an overview of the MAQL (Multi-Dimension Analytical Query Language) dialect that you use in GoodData and contrast it with SQL? What are the benefits and additional functionality that MAQL provides? When is GoodData the wrong choice? What is on the roadmap for the future of GoodData? Contact Info Sheila LinkedIn Philip LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links GoodData Teradata ReactJS SnowflakeDB Podcast Episode Redshift BigQuery SOC2 HIPAA GDPR == General Data Protection Regulation IoT == Internet of Things SAML Ruby Multi-Dimension Analytical Query Language Kubernetes The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Machine learning is a process driven by iteration and experimentation which requires fast and easy access to relevant features of the data being processed. In order to reduce friction in the process of developing and delivering models there has been a recent trend toward building a dedicated feature. In this episode Simba Khadder discusses his work at StreamSQL building a feature store to make creation, discovery, and monitoring of features fast and easy to manage. He describes the architecture of the system, the benefits of streaming data for machine learning, and how a feature store provides a useful interface between data engineers and machine learning engineers to reduce communication overhead. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Your host is Tobias Macey and today I’m interviewing Simba Khadder about his views on the importance of ML feature stores, and his experience implementing one at StreamSQL Interview Introduction How did you get involved in the areas of machine learning and data management? What is StreamSQL and what motivated you to start the business? Can you describe what a machine learning feature is? What is the difference between generating features for training a model and generating features for serving? How is feature management typically handled today? What is a feature store and how is it different from the status quo? What is the overall lifecycle of identifying useful features, defining and generating them, using them for training, and then serving them in production? How does the usage of a feature store impact the workflow of ML engineers/data scientists and data engineers? What are the general requirements of a feature store? What additional capabilities or tangential services are necessary for providing a pleasant UX for a feature store? How is discovery and documentation of features handled? What is the current landscape of feature stores and how does StreamSQL compare? How is the StreamSQL feature store implemented? How is the supporting infrastructure architected and how has it evolved since you first began working on it? Why is streaming data such a focal point of feature stores? How do you generate features for training? How do you approach monitoring of features and what does remediation look like for a feature that is no longer valid? How do you handle versioning and deploying features? What’s the process for integrating data sources into StreamSQL for processing into features? How are the features materialized? What are the most challenging or complex aspects of working on or with a feature store? When is StreamSQL the wrong choice for a feature store? What are the most interesting, challenging, or unexpected lessons that you have learned in the process of building StreamSQL? What do you have planned for the future of the product? Contact Info LinkedIn @simba_khadder on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links StreamSQL Feature Stores for ML Distributed Systems Google Cloud Datastore Triton Uber Michelangelo AirBnB Zipline Lyft Dryft Apache Flink Podcast Episode Apache Kafka Spark Streaming Apache Cassandra Redis Apache Pulsar Podcast Episode StreamNative Episode TDD == Test Driven Development Lyft presentation – Bootstrapping Flink Go-Jek Feast Hopsworks The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The landscape of data management and processing is rapidly changing and evolving. There are certain foundational elements that have remained steady, but as the industry matures new trends emerge and gain prominence. In this episode Astasia Myers of Redpoint Ventures shares her perspective as an investor on which categories she is paying particular attention to for the near to medium term. She discusses the work being done to address challenges in the areas of data quality, observability, discovery, and streaming. This is a useful conversation to gain a macro perspective on where businesses are looking to improve their capabilities to work with data. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar to get you up and running in no time. With simple pricing, fast networking, S3 compatible object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Astasia Myers about the trends in the data industry that she sees as an investor at Redpoint Ventures Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of Redpoint Ventures and your role there? From an investor perspective, what is most appealing about the category of data-oriented businesses? What are the main sources of information that you rely on to keep up to date with what is happening in the data industry? What is your personal heuristic for determining the relevance of any given piece of information to decide whether it is worthy of further investigation? As someone who works closely with a variety of companies across different industry verticals and different areas of focus, what are some of the common trends that you have identified in the data ecosystem? In your article that covers the trends you are keeping an eye on for 2020 you call out 4 in particular, data quality, data catalogs, observability of what influences critical business indicators, and streaming data. Taking those in turn: What are the driving factors that influence data quality, and what elements of that problem space are being addressed by the companies you are watching? What are the unsolved areas that you see as being viable for newcomers? What are the challenges faced by businesses in establishing and maintaining data catalogs? What approaches are being taken by the companies who are trying to solve this problem? What shortcomings do you see in the available products? For gaining visibility into the forces that impact the key performance indicators (KPI) of businesses, what is lacking in the current approaches? What additional information needs to be tracked to provide the needed context for making informed decisions about what actions to take to improve KPIs? What challenges do businesses in this observability space face to provide useful access and analysis to this collected data? Streaming is an area that has been growing rapidly over the past few years, with many open source and commercial options. What are the major business opportunities that you see to make streaming more accessible and effective? What are the main factors that you see as driving this growth in the need for access to streaming data? With your focus on these trends, how does that influence your investment decisions and where you spend your time? What are the unaddressed markets or product categories that you see which would be lucrative for new businesses? In most areas of technology now there is a mix of open source and commercial solutions to any given problem, with varying levels of maturity and polish between them. What are your views on the balance of this relationship in the data ecosystem? For data in particular, there is a strong potential for vendor lock-in which can cause potential customers to avoid adoption of commercial solutions. What has been your experience in that regard with the companies that you work with? Contact Info @AstasiaMyers on Twitter @astasia on Medium LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Redpoint Ventures 4 Data Trends To Watch in 2020 Seagate Western Digital Pure Storage Cisco Cohesity Looker Podcast Episode DGraph Podcast Episode Dremio Podcast Episode SnowflakeDB Podcast Episode Thoughspot Tibco Elastic Splunk Informatica Data Council DataCoral Mattermost Bitwarden Snowplow Podcast Interview Interview About Snowplow Infrastructure CHAOSSEARCH Podcast Episode Kafka Streams Pulsar Podcast Interview Followup Podcast Interview Soda Toro Great Expectations Alation Collibra Amundsen DataHub Netflix Metacat Marquez Podcast Episode LDAP == Lightweight Directory Access Protocol Anodot Databricks Flink Podcast Episode Zookeeper Podcast Episode Pravega Podcast Episode Airtable Alteryx CockroachDB Podcast Episode Superset The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Data lakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert. In order to bring the DBA into the new era of data management the team at Upsolver added a SQL interface to their data lake platform. In this episode Upsolver CEO Ori Rafael and CTO Yoni Iny describe how they have grown their platform deliberately to allow for layering SQL on top of a robust foundation for creating and operating a data lake, how to bring more people on board to work with the data being collected, and the unique benefits that a data lake provides. This was an interesting look at the impact that the interface to your data can have on who is empowered to work with it. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Ori Rafael and Yoni Iny about building a data lake for the DBA at Upsolver Interview Introduction How did you get involved in the area of data management? Can you start by sharing your definition of what a data lake is and what it is comprised of? We talked last in November of 2018. How has the landscape of data lake technologies and adoption changed in that time? How has Upsolver changed or evolved since we last spoke? How has the evolution of the underlying technologies impacted your implementation and overall product strategy? What are some of the common challenges that accompany a data lake implementation? How do those challenges influence the adoption or viability of a data lake? How does the introduction of a universal SQL layer change the staffing requirements for building and maintaining a data lake? What are the advantages of a data lake over a data warehouse if everything is being managed via SQL anyway? What are some of the underlying realities of the data systems that power the lake which will eventually need to be understood by the operators of the platform? How is the SQL layer in Upsolver implemented? What are the most challenging or complex aspects of managing the underlying technologies to provide automated partitioning, indexing, etc.? What are the main concepts that you need to educate your customers on? What are some of the pitfalls that users should be aware of? What features of your platform are often overlooked or underutilized which you think should be more widely adopted? What have you found to be the most interesting, unexpected, or challenging lessons learned while building the technical and business elements of Upsolver? What do you have planned for the future? Contact Info Ori LinkedIn Yoni yoniiny on GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Upsolver Podcast Episode DBA == Database Administrator IDF == Israel Defense Forces Data Lake Eventual Consistency Apache Spark Redshift Spectrum Azure Synapse Analytics SnowflakeDB Podcast Episode BigQuery Presto Podcast Episode Apache Kafka Cartesian Product kSQLDB Podcast Episode Eventador Podcast Episode Materialize Podcast Episode Common Table Expressions Lambda Architecture Kappa Architecture Apache Flink Podcast Episode Reinforcement Learning Cloudformation GDPR The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Gaining a complete view of the customer journey is especially difficult in B2B companies. This is due to the number of different individuals involved and the myriad ways that they interface with the business. Dreamdata integrates data from the multitude of platforms that are used by these organizations so that they can get a comprehensive view of their customer lifecycle. In this episode Ole Dallerup explains how Dreamdata was started, how their platform is architected, and the challenges inherent to data management in the B2B space. This conversation is a useful look into how data engineering and analytics can have a direct impact on the success of the business. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Ole Dallerup about Dreamdata, a platform for simplifying data integration for B2B companies Interview Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Dreamata? What was your inspiration for starting a company and what keeps you motivated? How do the data requirements differ between B2C and B2B companies? What are the challenges that B2B companies face in gaining visibility across the lifecycle of their customers? How does that lack of visibility impact the viability or growth potential of the business? What are the factors that contribute to silos in visibility of customer activity within a business? What are the data sources that you are dealing with to generate meaningful analytics for your customers? What are some of the challenges that business face in either generating or collecting useful information about their customer interactions? How is the technical platform of Dreamdata implemented and how has it evolved since you first began working on it? What are some of the ways that you approach entity resolution across the different channels and data sources? How do you reconcile the information collected from different sources that might use disparate data formats and representations? What is the onboarding process for your customers to identify and integrate with all of their systems? How do you approach the definition of the schema model for the database that your customers implement for storing their footprint? Do you allow for customization by the customer? Do you rely on a tool such as DBT for populating the table definitions and transformations from the source data? How do you approach representation of the analysis and actionable insights to your customers so that they are able to accurately intepret the results? How have your own experiences at Dreamdata influenced the areas that you invest in for the product? What are some of the most interesting or surprising insights that you have been able to gain as a result of the unified view that you are building? What are some of the most challenging, interesting, or unexpected lessons that you have learned from building and growing the technical and business elements of Dreamdata? When might a user be better served by building their own pipelines or analysis for tracking their customer interactions? What do you have planned for the future of Dreamdata? What are some of the industry trends that you are keeping an eye on and what potential impacts to your business do you anticipate? Contact Info LinkedIn @oledallerup on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Dreamdata Poker Tracker TrustPilot Zendesk Salesforce Hubspot Google BigQuery SnowflakeDB Podcast Episode AWS Redshift Singer Stitch Data Dataform Podcast Episode DBT Podcast Episode Segment Podcast Episode Cloud Dataflow Apache Beam UTM Parameters Clearbit Capterra G2 Crowd The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The PostgreSQL database is massively popular due to its flexibility and extensive ecosystem of extensions, but it is still not the first choice for high performance analytics. Swarm64 aims to change that by adding support for advanced hardware capabilities like FPGAs and optimized usage of modern SSDs. In this episode CEO and co-founder Thomas Richter discusses his motivation for creating an extension to optimize Postgres hardware usage, the benefits of running your analytics on the same platform as your application, and how it works under the hood. If you are trying to get more performance out of your database then this episode is for you! Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to dataengineeringpodcast.com/tidydata today and get started for free with no credit card required. Your host is Tobias Macey and today I’m interviewing Thomas Richter about Swarm64, a PostgreSQL extension to improve parallelism and add support for FPGAs Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Swarm64 is? How did the business get started and what keeps you motivated? What are some of the common bottlenecks that users of postgres run into? What are the use cases and workloads that gain the most benefit from increased parallelism in the database engine? By increasing the processing throughput of the database, how does that impact disk I/O and what are some options for avoiding bottlenecks in the persistence layer? Can you describe how Swarm64 is implemented? How has the product evolved since you first began working on it? How has the evolution of postgres impacted your product direction? What are some of the notable challenges that you have dealt with as a result of upstream changes in postgres? How has the hardware landscape evolved and how does that affect your prioritization of features and improvements? What are some of the other extensions in the postgres ecosystem that are most commonly used alongside Swarm64? Which extensions conflict with yours and how does that impact potential adoption? In addition to your work to optimize performance of the postres engine, you also provide support for using an FPGA as a co-processor. What are the benefits that an FPGA provides over and above a CPU or GPU architecture? What are the available options for provisioning hardware in a datacenter or the cloud that has access to an FPGA? Most people are familiar with the relevant attributes for selecting a CPU or GPU, what are the specifications that they should be looking at when selecting an FPGA? For users who are adopting Swarm64, how does it impact the way they should be thinking of their data models? What is involved in migrating an existing database to use Swarm64? What are some of the most interesting, unexpected, or challenging lessons that you have learned while building and growing the product and business of Swarm64? When is Swarm64 the wrong choice? What do you have planned for the future of Swarm64? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Swarm64 Lufthansa Cargo IBM Cognos Analytics OLAP Cube PostgreSQL Geospatial Data TimescaleDB Podcast Episode FPGA == Field Programmable Gate Array Greenplum Foreign Data Tables PostgreSQL Table Storage API EnterpriseDB Xilinx OVH Cloud Nimbix Azure Tableau The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary There have been several generations of platforms for managing streaming data, each with their own strengths and weaknesses, and different areas of focus. Pulsar is one of the recent entrants which has quickly gained adoption and an impressive set of capabilities. In this episode Sijie Guo discusses his motivations for spending so much of his time and energy on contributing to the project and growing the community. His most recent endeavor at StreamNative is focused on combining the capabilities of Pulsar with the cloud native movement to make it easier to build and scale real time messaging systems with built in event processing capabilities. This was a great conversation about the strengths of the Pulsar project, how it has evolved in recent years, and some of the innovative ways that it is being used. Pulsar is a well engineered and robust platform for building the core of any system that relies on durable access to easily scalable streams of data. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to dataengineeringpodcast.com/tidydata today and get started for free with no credit card required. Your host is Tobias Macey and today I’m interviewing Sijie Guo about the current state of the Pulsar framework for stream processing and his experiences building a managed offering for it at StreamNative Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of what Pulsar is? How did you get involved with the project? What is Pulsar’s role in the lifecycle of data and where does it fit in the overall ecosystem of data tools? How has the Pulsar project evolved or changed over the past 2 years? How has the overall state of the ecosystem influenced the direction that Pulsar has taken? One of the critical elements in the success of a piece of technology is the ecosystem that grows around it. How has the community responded to Pulsar, and what are some of the barriers to adoption? How are you and other project leaders addressing those barriers? You were a co-founder at Streamlio, which was built on top of Pulsar, and now you have founded StreamNative to offer Pulsar as a service. What did you learned from your time at Streamlio that has been most helpful in your current endeavor? How would you characterize your relationship with the project and community in each role? What motivates you to dedicate so much of your time and enery to Pulsar in particular, and the streaming data ecosystem in general? Why is streaming data such an important capability? How have projects such as Kafka and Pulsar impacted the broader software and data landscape? What are some of the most interesting, innovative, or unexpected ways that you have seen Pulsar used? When is Pulsar the wrong choice? What do you have planned for the future of StreamNative? Contact Info LinkedIn @sijieg on Twitter sijie on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Apache Pulsar Podcast Episode StreamNative Streamlio Hadoop HBase Hive Tencent Yahoo BookKeeper Publish/Subscribe Kafka Zookeeper Podcast Episode Kafka Connect Pulsar Functions Pulsar IO Kafka On Pulsar Webinar Video Pulsar Protocol Handler OVH Cloud Open Messaging ActiveMQ Kubernetes Helm Pulsar Helm Charts Grafana BestPay(?) Lambda Architecture Event Sourcing WebAssembly Apache Flink Podcast Episode Pulsar Summit The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Data management is hard at any scale, but working in the context of an enterprise organization adds even greater complexity. Infoworks is a platform built to provide a unified set of tooling for managing the full lifecycle of data in large businesses. By reducing the barrier to entry with a graphical interface for defining data transformations and analysis, it makes it easier to bring the domain experts into the process. In this interview co-founder and CTO of Infoworks Amar Arsikere explains the unique challenges faced by enterprise organizations, how the platform is architected to provide the needed flexibility and scale, and how a unified platform for data improves the outcomes of the organizations using it. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Free yourself from maintaining brittle data pipelines that require excessive coding and don’t operationally scale. With the Ascend Unified Data Engineering Platform, you and your team can easily build autonomous data pipelines that dynamically adapt to changes in data, code, and environment — enabling 10x faster build velocity and automated maintenance. On Ascend, data engineers can ingest, build, integrate, run, and govern advanced data pipelines with 95% less code. Go to dataengineeringpodcast.com/ascend to start building with a free 30-day trial. You’ll partner with a dedicated data engineer at Ascend to help you get started and accelerate your journey from prototype to production. Your host is Tobias Macey and today I’m interviewing Amar Arsikere about the Infoworks platform for enterprise data operations and orchestration Interview Introduction How did you get involved in the area of data management? Can you start by describing what you have built at Infoworks and the story of how it got started? What are the fundamental challenges that often plague organizations dealing with "big data"? How do those challenges change or compound in the context of an enterprise organization? What are some of the unique needs that enterprise organizations have of their data? What are the design or technical limitations of existing big data technologies that contribute to the overall difficulty of using or integrating them effectively? What are some of the tools or platforms that InfoWorks replaces in the overall data lifecycle? How do you identify and prioritize the integrations that you build? How is Infoworks itself architected and how has it evolved since you first built it? Discoverability and reuse of data is one of the biggest challenges facing organizations of all sizes. How do you address that in your platform? What are the roles that use InfoWorks in their day-to-day? What does the workflow look like for each of those roles? Can you talk through the overall lifecycle of a unit of data in InfoWorks and the different subsystems that it interacts with at each stage? What are some of the design challenges that you face in building a UI oriented workflow while providing the necessary level of control for these systems? How do you handle versioning of pipelines and validation of new iterations prior to production release? What are the cases where the no code, graphical paradigm for data orchestration breaks down? What are some of the most challenging, interesting, or unexpected lessons that you have learned since starting Infoworks? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links InfoWorks Google BigTable Apache Spark Apache Hadoop Zynga Data Partitioning Informatica Pentaho Talend Apache NiFi GoldenGate BigQuery Change Data Capture Podcast Episode About Debezium Slowly Changing Dimensions Snowflake DB Podcast Episode Tableau Data Catalog The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Data is a critical element to every role in an organization, which is also what makes managing it so challenging. With so many different opinions about which pieces of information are most important, how it needs to be accessed, and what to do with it, many data projects are doomed to failure. In this episode Chris Bergh explains how taking an agile approach to delivering value can drive down the complexity that grows out of the varied needs of the business. Building a DataOps workflow that incorporates fast delivery of well defined projects, continuous testing, and open lines of communication is a proven path to success. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! If DataOps sounds like the perfect antidote to your pipeline woes, DataKitchen is here to help. DataKitchen’s DataOps Platform automates and coordinates all the people, tools, and environments in your entire data analytics organization – everything from orchestration, testing and monitoring to development and deployment. In no time, you’ll reclaim control of your data pipelines so you can start delivering business value instantly, without errors. Go to dataengineeringpodcast.com/datakitchen today to learn more and thank them for supporting the show! Your host is Tobias Macey and today I’m welcoming back Chris Bergh to talk about ways that DataOps principles can help to reduce organizational complexity Interview Introduction How did you get involved in the area of data management? How are typical data and analytic teams organized? What are their roles and structure? Can you start by giving an outline of the ways that complexity can manifest in a data organization? What are some of the contributing factors that generate this complexity? How does the size or scale of an organization and their data needs impact the segmentation of responsibilities and roles? How does this organizational complexity play out within a single team? For example between data engineers, data scientists, and production/operations? How do you approach the definition of useful interfaces between different roles or groups within an organization? What are your thoughts on the relationship between the multivariate complexities of data and analytics workflows and the software trend toward microservices as a means of addressing the challenges of organizational communication patterns in the software lifecycle? How does this organizational complexity play out between multiple teams? For example between centralized data team and line of business self service teams? Isn’t organizational complexity just ‘the way it is’? Is there any how in getting out of meetings and inter team conflict? What are some of the technical elements that are most impactful in reducing the time to delivery for different roles? What are some strategies that you have found to be useful for maintaining a connection to the business need throughout the different stages of the data lifecycle? What are some of the signs or symptoms of problematic complexity that individuals and organizations should keep an eye out for? What role can automated testing play in improving this process? How do the current set of tools contribute to the fragmentation of data workflows? Which set of technologies are most valuable in reducing complexity and fragmentation? What advice do you have for data engineers to help with addressing complexity in the data organization and the problems that it contributes to? Contact Info LinkedIn @ChrisBergh on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links DataKitchen DataOps NASA Ames Research Center Excel Tableau Looker Podcast Episode Alteryx Trifacta Paxata AutoML Informatica SAS Conway’s Law Random Forest K-Means Clustering GraphQL Microservices Intuit Superglue Amundsen Podcast Episode Master Data Management Podcast Episode Hadoop Great Expectations Podcast Episode Observability Continuous Integration Continuous Delivery W. Edwards Deming The Joel Test Joel Spolsky DataOps Blog The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Modern applications frequently require access to real-time data, but building and maintaining the systems that make that possible is a complex and time consuming endeavor. Eventador is a managed platform designed to let you focus on using the data that you collect, without worrying about how to make it reliable. In this episode Eventador Founder and CEO Kenny Gorman describes how the platform is architected, the challenges inherent to managing reliable streams of data, the simplicity offered by a SQL interface, and the interesting projects that his customers have built on top of it. This was an interesting inside look at building a business on top of open source stream processing frameworks and how to reduce the burden on end users. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Your host is Tobias Macey and today I’m interviewing Kenny Gorman about the Eventador streaming SQL platform Interview Introduction How did you get involved in the area of data management? Can you start by describing what the Eventador platform is and the story behind it? How has your experience at ObjectRocket influenced your approach to streaming SQL? How do the capabilities and developer experience of Eventador compare to other streaming SQL engines such as ksqlDB, Pulsar SQL, or Materialize? What are the main use cases that you are seeing people use for streaming SQL? How does it fit into an application architecture? What are some of the design changes in the different layers that are necessary to take advantage of the real time capabilities? Can you describe how the Eventador platform is architected? How has the system design evolved since you first began working on it? How has the overall landscape of streaming systems changed since you first began working on Eventador? If you were to start over today what would you do differently? What are some of the most interesting and challenging operational aspects of running your platform? What are some of the ways that you have modified or augmented the SQL dialect that you support? What is the tipping point for when SQL is insufficient for a given task and a user might want to leverage Flink? What is the workflow for developing and deploying different SQL jobs? How do you handle versioning of the queries and integration with the software development lifecycle? What are some data modeling considerations that users should be aware of? What are some of the sharp edges or design pitfalls that users should be aware of? What are some of the most interesting, innovative, or unexpected ways that you have seen your customers use your platform? What are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of building and scaling Eventador? What do you have planned for the future of the platform? Contact Info LinkedIn Blog @kennygorman on Twitter kgorman on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Eventador Oracle DB Paypal EBay Semaphore MongoDB ObjectRocket RackSpace RethinkDB Apache Kafka Pulsar PostgreSQL Write-Ahead Log (WAL) ksqlDB Podcast Episode Pulsar SQL Materialize Podcast Episode PipelineDB Podcast Episode Apache Flink Podcast Episode Timely Dataflow FinTech == Financial Technology Anomaly Detection Network Security Materialized View Kubernetes Confluent Schema Registry Podcast Episode ANSI SQL Apache Calcite PostgreSQL User Defined Functions Change Data Capture Podcast Episode AWS Kinesis Uber AthenaX Netflix Keystone Ververica Rockset Podcast Episode Backpressure Keen.io The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The software applications that we build for our businesses are a rich source of data, but accessing and extracting that data is often a slow and error-prone process. Rookout has built a platform to separate the data collection process from the lifecycle of your code. In this episode, CTO Liran Haimovitch discusses the benefits of shortening the iteration cycle and bringing non-engineers into the process of identifying useful data. This was a great conversation about the importance of democratizing the work of data collection. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Your host is Tobias Macey and today I’m interviewing Liran Haimovitch, CTO of Rookout, about the business value of operations metrics and other dark data in your organization Interview Introduction How did you get involved in the area of data management? Can you start by describing the types of data that we typically collect for the systems operations context? What are some of the business questions that can be answered from these data sources? What are some of the considerations that developers and operations engineers need to be aware of when they are defining the collection points for system metrics and log messages? What are some effective strategies that you have found for including business stake holders in the process of defining these collection points? One of the difficulties in building useful analyses from any source of data is maintaining the appropriate context. What are some of the necessary metadata that should be maintained along with operational metrics? What are some of the shortcomings in the systems we design and use for operational data stores in terms of making the collected data useful for other purposes? How does the existing tooling need to be changed or augmented to simplify the collaboration between engineers and stake holders for defining and collecting the needed information? The types of systems that we use for collecting and analyzing operations metrics are often designed and optimized for different access patterns and data formats than those used for analytical and exploratory purposes. What are your thoughts on how to incorporate the collected metrics with behavioral data? What are some of the other sources of dark data that we should keep an eye out for in our organizations? Contact Info LinkedIn @Liran_Last on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Rookout Cybersecurity DevOps DataDog Graphite Elasticsearch Logz.io Kafka The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Knowledge graphs are a data resource that can answer questions beyond the scope of traditional data analytics. By organizing and storing data to emphasize the relationship between entities, we can discover the complex connections between multiple sources of information. In this episode John Maiden talks about how Cherre builds knowledge graphs that provide powerful insights for their customers and the engineering challenges of building a scalable graph. If you’re wondering how to extract additional business value from existing data, this episode will provide a way to expand your data resources. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. We have partnered with organizations such as ODSC, and Data Council. Upcoming events include ODSC East which has gone virtual starting April 16th. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing John Maiden about how Cherre is building and using a knowledge graph of commercial real estate information Interview Introduction How did you get involved in the area of data management? Can you start by describing what Cherre is and the role that data plays in the business? What are the benefits of a knowledge graph for making real estate investment decisions? What are the main ways that you and your customers are using the knowledge graph? What are some of the challenges that you face in providing a usable interface for end-users to query the graph? What technology are you using for storing and processing the graph? What challenges do you face in scaling the complexity and analysis of the graph? What are the main sources of data for the knowledge graph? What are some of the ways that messiness manifests in the data that you are using to populate the graph? How are you managing cleaning of the data and how do you identify and process records that can’t be coerced into the desired structure? How do you handle missing attributes or extra attributes in a given record? How did you approach the process of determining an effective taxonomy for records in the graph? What is involved in performing entity extraction on your data? What are some of the most interesting or unexpected questions that you have been able to ask and answer with the graph? What are some of the most interesting/unexpected/challenging lessons that you have learned in the process of working with this data? What are some of the near and medium term improvements that you have planned for your knowledge graph? What advice do you have for anyone who is interested in building a knowledge graph of their own? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Cherre Commercial Real Estate Knowledge Graph RDF Triple DGraph Podcast Interview Neo4J TigerGraph Google BigQuery Apache Spark Spark In Action Episode Entity Extraction/Named Entity Recognition NetworkX Spark Graph Frames Graph Embeddings Airflow Podcast.__init__ Interview DBT The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Building and maintaining a system that integrates and analyzes all of the data for your organization is a complex endeavor. Operating on a shoe-string budget makes it even more challenging. In this episode Tyler Colby shares his experiences working as a data professional in the non-profit sector. From managing Salesforce data models to wrangling a multitude of data sources and compliance challenges, he describes the biggest challenges that he is facing. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference and ODSC East which has also gone virtual. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Tyler Colby about his experiences working as a data professional in the non-profit arena, most recently at the Natural Resources Defense Council Interview Introduction How did you get involved in the area of data management? Can you start by describing your responsibilities as the director of data infrastructure at the NRDC? What specific challenges are you facing at the NRDC? Can you describe some of the types of data that you are working with at the NRDC? What types of systems are you relying on for the source of your data? What kinds of systems have you put in place to manage the data needs of the NRDC? What are your biggest influences in the build vs. buy decisions that you make? What heuristics or guidelines do you rely on for aligning your work with the business value that it will produce and the broader mission of the organization? Have you found there to be any extra scrutiny of your work as a member of a non-profit in terms of regulations or compliance questions? Your career has involved a significant focus on the Salesforce platform. For anyone not familiar with it, what benefits does it provide in managing information flows and analysis capabilities? What are some of the most challenging or complex aspects of working with Saleseforce? In light of the current global crisis posed by COVID-19 you have established a new non-profit entity to organize the efforts of various technical professionals. Can you describe the nature of that mission? What are some of the unique data challenges that you anticipate or have already encountered? How do the data challenges of this new organization compare to your past experiences? What have you found to be most useful or beneficial in the current landscape of data management systems and practices in your career with non-profit organizations? What are the areas that need to be addressed or improved for workers in the non-profit sector? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links NRDC AWS Redshift Time Warner Cable Salesforce Cloud For Good Tableau Civis Analytics EveryAction BlackBaud ActionKit MobileCommons XKCD 1667 GDPR == General Data Privacy Regulation CCPA == California Consumer Privacy Act Salesforce Apex Salesforce.org Salesforce Non-Profit Success Pack Validity OpenRefine JitterBit Skyvia The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary There are a number of platforms available for object storage, including self-managed open source projects. But what goes on behind the scenes of the companies that run these systems at scale so you don’t have to? In this episode Will Smith shares the journey that he and his team at Linode recently completed to bring a fast and reliable S3 compatible object storage to production for your benefit. He discusses the challenges of running object storage for public usage, some of the interesting ways that it was stress tested internally, and the lessons that he learned along the way. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Will Smith about his work on building object storage for the Linode cloud platform Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the current state of your object storage product? What was the motivating factor for building and managing your own object storage system rather than building an integration with another offering such as Wasabi or Backblaze? What is the scale and scope of usage that you had to design for? Can you describe how your platform is implemented? What was your criteria for deciding whether to use an available platform such as Ceph or MinIO vs building your own from scratch? How have your initial assumptions about the operability and maintainability of your installation been challenged or updated since it has been released to the public? What have been the biggest challenges that you have faced in designing and deploying a system that can meet the scale and reliability requirements of Linode? What are the most important capabilities for the underlying hardware that you are running on? What supporting systems and tools are you using to manage the availability and durability of your object storage? How did you approach the rollout of Linode’s object storage to gain the confidence that you needed to feel comfortable with full scale usage? What are some of the benefits that you have gained internally at Linode from having an object storage system available to your product teams? What are your thoughts on the state of the S3 API as a de facto standard for object storage? What is your main focus now that object storage is being rolled out to more data centers? Contact Info Dorthu on GitHub dorthu22 on Twitter LinkedIn Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Linode Object Storage Xen Hypervisor KVM (Linux Kernel Virtual Machine) Linode API V4 Ceph Distributed Filesystem Podcast Episode Wasabi Backblaze MinIO CERN Ceph Scaling Paper RADOS Gateway OpenResty Lua Prometheus Linode Managed Kubernetes Ceph Swift Protocol Ceph Bug Tracker Linode Dashboard Application Source Code The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary CouchDB is a distributed document database built for scale and ease of operation. With a built-in synchronization protocol and a HTTP interface it has become popular as a backend for web and mobile applications. Created 15 years ago, it has accrued some technical debt which is being addressed with a refactored architecture based on FoundationDB. In this episode Adam Kocoloski shares the history of the project, how it works under the hood, and how the new design will improve the project for our new era of computation. This was an interesting conversation about the challenges of maintaining a large and mission critical project and the work being done to evolve it. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Are you spending too much time maintaining your data pipeline? Snowplow empowers your business with a real-time event data pipeline running in your own cloud account without the hassle of maintenance. Snowplow takes care of everything from installing your pipeline in a couple of hours to upgrading and autoscaling so you can focus on your exciting data projects. Your team will get the most complete, accurate and ready-to-use behavioral web and mobile data, delivered into your data warehouse, data lake and real-time streams. Go to dataengineeringpodcast.com/snowplow today to find out why more than 600,000 websites run Snowplow. Set up a demo and mention you’re a listener for a special offer! Setting up and managing a data warehouse for your business analytics is a huge task. Integrating real-time data makes it even more challenging, but the insights you obtain can make or break your business growth. You deserve a data warehouse engine that outperforms the demands of your customers and simplifies your operations at a fraction of the time and cost that you might expect. You deserve ClickHouse, the open-source analytical database that deploys and scales wherever and whenever you want it to and turns data into actionable insights. And Altinity, the leading software and service provider for ClickHouse, is on a mission to help data engineers and DevOps managers tame their operational analytics. Go to dataengineeringpodcast.com/altinity for a free consultation to find out how they can help you today. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Adam Kocoloski about CouchDB and the work being done to migrate the storage layer to FoundationDB Interview Introduction How did you get involved in the area of data management? Can you starty by describing what CouchDB is? How did you get involved in the CouchDB project and what is your current role in the community? What are the use cases that it is well suited for? Can you share some of the history of CouchDB and its role in the NoSQL movement? How is CouchDB currently architected and how has it evolved since it was first introduced? What have been the benefits and challenges of Erlang as the runtime for CouchDB? How is the current storage engine implemented and what are its shortcomings? What problems are you trying to solve by replatforming on a new storage layer? What were the selection criteria for the new storage engine and how did you structure the decision making process? What was the motivation for choosing FoundationDB as opposed to other options such as rocksDB, levelDB, etc.? How is the adoption of FoundationDB going to impact the overall architecture and implementation of CouchDB? How will the use of FoundationDB impact the way that the current capabilities are implemented, such as data replication? What will the migration path be for people running an existing installation? What are some of the biggest challenges that you are facing in rearchitecting the codebase? What new capabilities will the FoundationDB storage layer enable? What are some of the most interesting/unexpected/innovative ways that you have seen CouchDB used? What new capabilities or use cases do you anticipate once this migration is complete? What are some of the most interesting/unexpected/challenging lessons that you have learned while working with the CouchDB project and community? What is in store for the future of CouchDB? Contact Info LinkedIn @kocolosk on Twitter kocolosk on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Apache CouchDB FoundationDB Podcast Episode IBM Cloudant Experimental Particle Physics FPGA == Field Programmable Gate Array Apache Software Foundation CRDT == Conflict-free Replicated Data Type Podcast Episode Erlang Riak RabbitMQ Heisenbug Kubernetes Property Based Testing The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Data governance is a complex endeavor, but scaling it to meet the needs of a complex or globally distributed organization requires a well considered and coherent strategy. In this episode Tim Ward describes an architecture that he has used successfully with multiple organizations to scale compliance. By treating it as a graph problem, where each hub in the network has localized control with inheritance of higher level controls it reduces overhead and provides greater flexibility. Tim provides useful examples for understanding how to adopt this approach in your own organization, including some technology recommendations for making it maintainable and scalable. If you are struggling to scale data quality controls and governance requirements then this interview will provide some useful ideas to incorporate into your roadmap. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Tim Ward about using an architectural pattern called data hub that allows for scaling data management across global businesses Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the goals of a data hub architecture? What are the elements of a data hub architecture and how do they contribute to the overall goals? What are some of the patterns or reference architectures that you drew on to develop this approach? What are some signs that an organization should implement a data hub architecture? What is the migration path for an organization who has an existing data platform but needs to scale their governance and localize storage and access? What are the features or attributes of an individual hub that allow for them to be interconnected? What is the interface presented between hubs to allow for accessing information across these localized repositories? What is the process for adding a new hub and making it discoverable across the organization? How is discoverability of data managed within and between hubs? If someone wishes to access information between hubs or across several of them, how do you prevent data proliferation? If data is copied between hubs, how are record updates accounted for to ensure that they are replicated to the hubs that hold a copy of that entity? How are access controls and data masking managed to ensure that various compliance regimes are honored? In addition to compliance issues, another challenge of distributed data repositories is the question of latency. How do you mitigate the performance impacts of querying across multiple hubs? Given that different hubs can have differing rules for quality, cleanliness, or structure of a given record how do you handle transformations of data as it traverses different hubs? How do you address issues of data loss or corruption within those transformations? How is the topology of a hub infrastructure arranged and how does that impact questions of data loss through multiple zone transformations, latency, etc.? How do you manage tracking and reporting of data lineage within and across hubs? For an organization that is interested in implementing their own instance of a data hub architecture, what are the necessary components of an individual hub? What are some of the considerations and useful technologies that would assist in creating and connecting hubs? Should the hubs be implmeneted in a homogeneous fashion, or is there room for heterogeneity in their infrastructure as long as they expose the appropriate interface? When is a data hub architecture the wrong approach? Contact Info LinkedIn @jerrong on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links CluedIn Podcast Episode Eventual Connectivity Episode Futurama Kubernetes Zookeeper Podcast Episode Data Governance Data Lineage Data Sovereignty Graph Database Helm Chart Application Container Docker Compose LinkedIn DataHub Udemy PluralSight The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Building applications on top of unbounded event streams is a complex endeavor, requiring careful integration of multiple disparate systems that were engineered in isolation. The ksqlDB project was created to address this state of affairs by building a unified layer on top of the Kafka ecosystem for stream processing. Developers can work with the SQL constructs that they are familiar with while automatically getting the durability and reliability that Kafka offers. In this episode Michael Drogalis, product manager for ksqlDB at Confluent, explains how the system is implemented, how you can use it for building your own stream processing applications, and how it fits into the lifecycle of your data infrastructure. If you have been struggling with building services on low level streaming interfaces then give this episode a listen and try it out for yourself. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Are you spending too much time maintaining your data pipeline? Snowplow empowers your business with a real-time event data pipeline running in your own cloud account without the hassle of maintenance. Snowplow takes care of everything from installing your pipeline in a couple of hours to upgrading and autoscaling so you can focus on your exciting data projects. Your team will get the most complete, accurate and ready-to-use behavioral web and mobile data, delivered into your data warehouse, data lake and real-time streams. Go to dataengineeringpodcast.com/snowplow today to find out why more than 600,000 websites run Snowplow. Set up a demo and mention you’re a listener for a special offer! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Michael Drogalis about ksqlDB, the open source streaming database layer for Kafka Interview Introduction How did you get involved in the area of data management? Can you start by describing what ksqlDB is? What are some of the use cases that it is designed for? How do the capabilities and design of ksqlDB compare to other solutions for querying streaming data with SQL such as Pulsar SQL, PipelineDB, or Materialize? What was the motivation for building a unified project for providing a database interface on the data stored in Kafka? How is ksqlDB architected? If you were to rebuild the entire platform and its components from scratch today, what would you do differently? What is the workflow for an analyst or engineer to design and build an application on top of ksqlDB? What dialect of SQL is supported? What kinds of extensions or built in functions have been added to aid in the creation of streaming queries? How are table schemas defined and enforced? How do you handle schema migrations on active streams? Typically a database is considered a long term storage location for data, whereas Kafka is a streaming layer with a bounded amount of durable storage. What is a typical lifecycle of information in ksqlDB? Can you talk through an example architecture that might incorporate ksqlDB including the source systems, applications that might interact with the data in transit, and any destinations sytems for long term persistence? What are some of the less obvious features of ksqlDB or capabilities that you think should be more widely publicized? What are some of the edge cases or potential pitfalls that users should be aware of as they are designing their streaming applications? What is involved in deploying and maintaining an installation of ksqlDB? What are some of the operational characteristics of the system that should be considered while planning an installation such as scaling factors, high availability, or potential bottlenecks in the architecture? When is ksqlDB the wrong choice? What are some of the most interesting/unexpected/innovative projects that you have seen built with ksqlDB? What are some of the most interesting/unexpected/challenging lessons that you have learned while working on ksqlDB? What is in store for the future of the project? Contact Info @michaeldrogalis on Twitter michaeldrogalis on GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links ksqlDB Confluent Erlang Onyx Apache Storm Stream Processing Kafka ksql Kafka Streams Pulsar Podcast Episode Pulsar SQL PipelineDB Podcast Episode Materialize Podcast Episode Kafka Connect RocksDB Java Jar CLI == Command Line Interface PrestoDB Podcast Episode ANSI SQL Pravega Podcast Episode Eventual Consistency Confluent Cloud MySQL PostgreSQL GraphQL The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Misaligned priorities across business units can lead to tensions that drive members of the organization to build data and analytics projects without the guidance or support of engineering or IT staff. The availability of cloud platforms and managed services makes this a viable option, but can lead to downstream challenges. In this episode Sean Knapp and Charlie Crocker share their experiences of working in and with companies that have dealt with shadow IT projects and the importance of enabling and empowering the use and exploration of data and analytics. If you have ever been frustrated by seemingly draconian policies or struggled to align everyone on your supported platform, then this episode will help you gain some perspective and set you on a path to productive collaboration. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Are you spending too much time maintaining your data pipeline? Snowplow empowers your business with a real-time event data pipeline running in your own cloud account without the hassle of maintenance. Snowplow takes care of everything from installing your pipeline in a couple of hours to upgrading and autoscaling so you can focus on your exciting data projects. Your team will get the most complete, accurate and ready-to-use behavioral web and mobile data, delivered into your data warehouse, data lake and real-time streams. Go to dataengineeringpodcast.com/snowplow today to find out why more than 600,000 websites run Snowplow. Set up a demo and mention you’re a listener for a special offer! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Sean Knapp, Charlie Crocker about shadow IT in data and analytics Interview Introduction How did you get involved in the area of data management? Can you start by sharing your definition of shadow IT? What are some of the reasons that members of an organization might start building their own solutions outside of what is supported by the engineering teams? What are some of the roles in an organization that you have seen involved in these shadow IT projects? What kinds of tools or platforms are well suited for being provisioned and managed without involvement from the platform team? What are some of the pitfalls that these solutions present as a result of their initial ease of use? What are the benefits to the organization of individuals or teams building and managing their own solutions? What are some of the risks associated with these implementations of data collection, storage, management, or analysis that have no oversight from the teams typically tasked with managing those systems? What are some of the ways that compliance or data quality issues can arise from these projects? Once a project has been started outside of the approved channels it can quickly take on a life of its own. What are some of the ways you have identified the presence of "unauthorized" data projects? Once you have identified the existence of such a project how can you revise their implementation to integrate them with the "approved" platform that the organization supports? What are some strategies for removing the friction in the collection, access, or availability of data in an organization that can eliminate the need for shadow IT implementations? What are some of the inherent complexities in data management which you would like to see resolved in order to reduce the tensions that lead to these bespoke solutions? Contact Info Sean LinkedIn @seanknapp on Twitter Charlie LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Shadow IT Ascend Podcast Episode ZoneHaven Google Sawzall M&A == Mergers and Acquisitions DevOps Waterfall Development Data Governance Data Lineage Pioneers, Settlers, and Town Planners PowerBI Tableau Excel Amundsen Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary One of the biggest challenges in building reliable platforms for processing event pipelines is managing the underlying infrastructure. At Snowplow Analytics the complexity is compounded by the need to manage multiple instances of their platform across customer environments. In this episode Josh Beemster, the technical operations lead at Snowplow, explains how they manage automation, deployment, monitoring, scaling, and maintenance of their streaming analytics pipeline for event data. He also shares the challenges they face in supporting multiple cloud environments and the need to integrate with existing customer systems. If you are daunted by the needs of your data infrastructure then it’s worth listening to how Josh and his team are approaching the problem. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Josh Beemster about how Snowplow manages deployment and maintenance of their managed service in their customer’s cloud accounts. Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the components in your system architecture and the nature of your managed service? What are some of the challenges that are inherent to private SaaS nature of your managed service? What elements of your system require the most attention and maintenance to keep them running properly? Which components in the pipeline are most subject to variability in traffic or resource pressure and what do you do to ensure proper capacity? How do you manage deployment of the full Snowplow pipeline for your customers? How has your strategy for deployment evolved since you first began Soffering the managed service? How has the architecture of the pipeline evolved to simplify operations? How much customization do you allow for in the event that the customer has their own system that they want to use in place of one of your supported components? What are some of the common difficulties that you encounter when working with customers who need customized components, topologies, or event flows? How does that reflect in the tooling that you use to manage their deployments? What types of metrics do you track and what do you use for monitoring and alerting to ensure that your customers pipelines are running smoothly? What are some of the most interesting/unexpected/challenging lessons that you have learned in the process of working with and on Snowplow? What are some lessons that you can generalize for management of data infrastructure more broadly? If you could start over with all of Snowplow and the infrastructure automation for it today, what would you do differently? What do you have planned for the future of the Snowplow product and infrastructure management? Contact Info LinkedIn jbeemster on GitHub @jbeemster1 on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Snowplow Analytics Podcast Episode Terraform Consul Nomad Meltdown Vulnerability Spectre Vulnerability AWS Kinesis Elasticsearch SnowflakeDB Indicative S3 Segment AWS Cloudwatch Stackdriver Apache Kafka Apache Pulsar Google Cloud PubSub AWS SQS AWS SNS AWS Redshift Ansible AWS Cloudformation Kubernetes AWS EMR The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Designing the structure for your data warehouse is a complex and challenging process. As businesses deal with a growing number of sources and types of information that they need to integrate, they need a data modeling strategy that provides them with flexibility and speed. Data Vault is an approach that allows for evolving a data model in place without requiring destructive transformations and massive up front design to answer valuable questions. In this episode Kent Graziano shares his journey with data vault, explains how it allows for an agile approach to data warehousing, and explains the core principles of how to use it. If you’re struggling with unwieldy dimensional models, slow moving projects, or challenges integrating new data sources then listen in on this conversation and then give data vault a try for yourself. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Setting up and managing a data warehouse for your business analytics is a huge task. Integrating real-time data makes it even more challenging, but the insights you obtain can make or break your business growth. You deserve a data warehouse engine that outperforms the demands of your customers and simplifies your operations at a fraction of the time and cost that you might expect. You deserve Clickhouse, the open source analytical database that deploys and scales wherever and whenever you want it to and turns data into actionable insights. And Altinity, the leading software and service provider for Clickhouse, is on a mission to help data engineers and DevOps managers tame their operational analytics. Go to dataengineeringpodcast.com/altinity for a free consultation to find out how they can help you today. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Kent Graziano about data vault modeling and the role that it plays in the current data landscape Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of what data vault modeling is and how it differs from other approaches such as third normal form or the star/snowflake schema? What is the history of this approach and what limitations of alternate styles of modeling is it attempting to overcome? How did you first encounter this approach to data modeling and what is your motivation for dedicating so much time and energy to promoting it? What are some of the primary challenges associated with data modeling that contribute to the long lead times for data requests or outright project Datafailure? What are some of the foundational skills and knowledge that are necessary for effective modeling of data warehouses? How has the era of data lakes, unstructured/semi-structured data, and non-relational storage engines impacted the state of the art in data modeling? Is there any utility in data vault modeling in a data lake context (S3, Hadoop, etc.)? What are the steps for establishing and evolving a data vault model in an organization? How does that approach scale from one to many data sources and their varying lifecycles of schema changes and data loading? What are some of the changes in query structure that consumers of the model will need to plan for? Are there any performance or complexity impacts imposed by the data vault approach? Can you talk through the overall lifecycle of data in a data vault modeled warehouse? How does that compare to approaches such as audit/history tables in transaction databases or slowly changing dimensions in a star or snowflake model? What are some cases where a data vault approach doesn’t fit the needs of an organization or application? For listeners who want to learn more, what are some references or exercises that you recommend? Contact Info Website LinkedIn @KentGraziano on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links SnowflakeDB Data Vault Modeling Data Warrior Blog OLTP == On-Line Transaction Processing Data Warehouse Bill Inmon Claudia Imhoff Oracle DB Third Normal Form Star Schema Snowflake Schema Relational Theory Sixth Normal Form Denormalization Pivot Table Dan Linstedt TDAN.com Ralph Kimball Agile Manifesto Schema On Read Data Lake Hadoop NoSQL Data Vault Conference Teradata ODS (Operational Data Store) Model Supercharge Your Data Warehouse (affiliate link) Building A Scalable Data Warehouse With Data Vault 2.0 (affiliate link) Data Model Resource Book (affiliate link) Data Warehouse Toolkit (affiliate link) Building The Data Warehouse (affiliate link) Dan Linstedt Blog Perforrmance G2 Scale Free European Classes Certus Australian Classes Wherescape Erwin VaultSpeed Data Vault Builder Varigence BimlFlex The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Every business collects data in some fashion, but sometimes the true value of the collected information only comes when it is combined with other data sources. Data trusts are a legal framework for allowing businesses to collaboratively pool their data. This allows the members of the trust to increase the value of their individual repositories and gain new insights which would otherwise require substantial effort in duplicating the data owned by their peers. In this episode Tom Plagge and Greg Mundy explain how the BrightHive platform serves to establish and maintain data trusts, the technical and organizational challenges they face, and the outcomes that they have witnessed. If you are curious about data sharing strategies or data collaboratives, then listen now to learn more! Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Tom Plagge and Gregory Mundy about BrightHive, a platform for building data trusts Interview Introduction How did you get involved in the area of data management? Can you start by describing what a data trust is? Why might an organization want to build one? What is BrightHive and what is its origin story? Beyond having a storage location with access controls, what are the components of a data trust that are necessary for them to be viable? What are some of the challenges that are common in establishing an agreement among organizations who are participating in a data trust? What are the responsibilities of each of the participants in a data trust? For an individual or organization who wants to participate in an existing trust, what is involved in gaining access? How does BrightHive support the process of building a data trust? How is ownership of derivative data sets/data products and associated intellectual property handled in the context of a trust? How is the technical architecture of BrightHive implemented and how has it evolved since it first started? What are some of the ways that you approach the challenge of data privacy in these sharing agreements? What are some legal and technical guards that you implement to encourage ethical uses of the data contained in a trust? What is the motivation for releasing the technical elements of BrightHive as open source? What are some of the most interesting, innovative, or inspirational ways that you have seen BrightHive used? Being a shared platform for empowering other organizations to collaborate I imagine there is a strong focus on long-term sustainability. How are you approaching that problem and what is the business model for BrightHive? What have you found to be the most interesting/unexpected/challenging aspects of building and growing the technical and business infrastructure of BrightHive? What do you have planned for the future of BrightHive? Contact Info Tom LinkedIn tplagge on GitHub Gregory LinkedIn gregmundy on GitHub @graygoree on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links BrightHive Data Science For Social Good Workforce Data Initiative NASA NOAA Data Trust Data Collaborative Public Benefit Corporation Terraform Airflow Podcast.__init__ Episode Dagster Podcast Episode Secure Multi-Party Computation Public Key Encryption AWS Macie Blockchain Smart Contracts The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Data pipelines are complicated and business critical pieces of technical infrastructure. Unfortunately they are also complex and difficult to test, leading to a significant amount of technical debt which contributes to slower iteration cycles. In this episode James Campbell describes how he helped create the Great Expectations framework to help you gain control and confidence in your data delivery workflows, the challenges of validating and monitoring the quality and accuracy of your data, and how you can use it in your own environments to improve your ability to move fast. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing James Campbell about Great Expectations, the open source test framework for your data pipelines which helps you continually monitor and validate the integrity and quality of your data Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Great Expecations is and the origin of the project? What has changed in the implementation and focus of Great Expectations since we last spoke on Podcast.__init__ 2 years ago? Prior to your introduction of Great Expectations what was the state of the industry with regards to testing, monitoring, or validation of the health and quality of data and the platforms operating on them? What are some of the types of checks and assertions that can be made about a pipeline using Great Expectations? What are some of the non-obvious use cases for Great Expectations? What aspects of a data pipeline or the context that it operates in are unable to be tested or validated in a programmatic fashion? Can you describe how Great Expectations is implemented? For anyone interested in using Great Expectations, what is the workflow for incorporating it into their environments? What are some of the test cases that are often overlooked which data engineers and pipeline operators should be considering? Can you talk through some of the ways that Great Expectations can be extended? What are some notable extensions or integrations of Great Expectations? Beyond the testing and validation of data as it is being processed you have also included features that support documentation and collaboration of the data lifecycles. What are some of the ways that those features can benefit a team working with Great Expectations? What are some of the most interesting/innovative/unexpected ways that you have seen Great Expectations used? What are the limitations of Great Expectations? What are some cases where Great Expectations would be the wrong choice? What do you have planned for the future of Great Expectations? Contact Info LinkedIn @jpcampbell42 on Twitter jcampbell on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Great Expectations GitHub Twitter Podcast.__init__ Interview on Great Expectations Superconductive Health Abe Gong Pandas Podcast.__init__ Interview SQLAlchemy PostgreSQL Podcast Episode RedShift BigQuery Spark Cloudera DataBricks Great Expectations Data Docs Great Expectations Data Profiling Apache NiFi Amazon Deequ Tensorflow Data Validation The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Building a reliable data platform is a neverending task. Even if you have a process that works for you and your business there can be unexpected events that require a change in your platform architecture. In this episode the head of data for Mayvenn shares their experience migrating an existing set of streaming workflows onto the Ascend platform after their previous vendor was acquired and changed their offering. This is an interesting discussion about the ongoing maintenance and decision making required to keep your business data up to date and accurate. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Sheel Choksi and Sean Knapp about Mayvenn’s experience migrating their dataflows onto the Ascend platform Interview Introduction How did you get involved in the area of data management? Can you start off by describing what Mayvenn is and give a sense of how you are using data? What are the sources of data that you are working with? What are the biggest challenges you are facing in collecting, processing, and analyzing your data? Before adopting Ascend, what did your overall platform for data management look like? What were the pain points that you were facing which led you to seek a new solution? What were the selection criteria that you set forth for addressing your needs at the time? What were the aspects of Ascend which were most appealing? What are some of the edge cases that you have dealt with in the Ascend platform? Now that you have been using Ascend for a while, what components of your previous architecture have you been able to retire? Can you talk through the migration process of incorporating Ascend into your platform and any validation that you used to ensure that your data operations remained accurate and consistent? How has the migration to Ascend impacted your overall capacity for processing data or integrating new sources into your analytics? What are your future plans for how to use data across your organization? Contact Info Sheel LinkedIn sheelc on GitHub Sean LinkedIn @seanknapp on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Mayvenn Ascend Podcast Episode Google Sawzall Clickstream Apache Kafka Alooma Podcast Episode Amazon Redshift ELT == Extract, Load, Transform DBT Podcast Episode Amazon Data Pipeline Upsolver Pentaho Stitch Data Fivetran Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

SummaryThe modern era of software development is identified by ubiquitous access to elastic infrastructure for computation and easy automation of deployment. This has led to a class of applications that can quickly scale to serve users worldwide. This requires a new class of data storage which can accomodate that demand without having to rearchitect your system at each level of growth. YugabyteDB is an open source database designed to support planet scale workloads with high data density and full ACID compliance. In this episode Karthik Ranganathan explains how Yugabyte is architected, their motivations for being fully open source, and how they simplify the process of scaling your application from greenfield to global.AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementWhen you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.Your host is Tobias Macey and today I’m interviewing Karthik Ranganathan about YugabyteDB, the open source, high-performance distributed SQL database for global, internet-scale apps.InterviewIntroductionHow did you get involved in the area of data management?Can you start by describing what YugabyteDB is and its origin story?A growing trend in database engines (e.g. FaunaDB, CockroachDB) has been an out of the box focus on global distribution. Why is that important and how does it work in Yugabyte? What are the caveats?What are the most notable features of YugabyteDB that would lead someone to choose it over any of the myriad other options? What are the use cases that it is uniquely suited to?What are some of the systems or architecture patterns that can be replaced with Yugabyte?How does the design of Yugabyte or the different ways it is being used influence the way that users should think about modeling their data?Yugabyte is an impressive piece of engineering. Can you talk through the major design elements and how it is implemented?Easy scaling and failover is a feature that many database engines would like to be able to claim. What are the difficult elements that prevent them from implementing that capability as a standard practice? What do you have to sacrifice in order to support the level of scale and fault tolerance that you provide?Speaking of scaling, there are many ways to define that term, from vertical scaling of storage or compute, to horizontal scaling of compute, to scaling of reads and writes. What are the primary scaling factors that you focus on in Yugabyte?How do you approach testing and validation of the code given the complexity of the system that you are building?In terms of the query API you have support for a Postgres compatible SQL dialect as well as a Cassandra based syntax. What are the benefits of targeting compatibility with those platforms? What are the challenges and benefits of maintaining compatibility with those other platforms?Can you describe how the storage layer is implemented and the division between the different query formats?What are the operational characteristics of YugabyteDB? What are the complexities or edge cases that users should be aware of when planning a deployment?One of the challenges of working with large volumes of data is creating and maintaining backups. How does Yugabyte handle that problem?Most open source infrastructure projects that are backed by a business withhold various "enterprise" features such as backups and change data capture as a means of driving revenue. Can you talk through your motivation for releasing those capabilities as open source?What is the business model that you are using for YugabyteDB and how does it differ from the tribal knowledge of how open source companies generally work?What are some of the most interesting, innovative, or unexpected ways that you have seen yugabyte used?When is Yugabyte the wrong choice?What do you have planned for the future of the technical and business aspects of Yugabyte?Contact Info@karthikr on TwitterLinkedInrkarthik007 on GitHubParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersJoin the community in the new Zulip chat workspace at dataengineeringpodcast.com/chatLinksYugabyteDBGitHubNutanixFacebook EngineeringApache CassandraApache HBaseDelphiFuanaDBPodcast EpisodeCockroachDBPodcast EpisodeHA == High AvailabilityOracleMicrosoft SQL ServerPostgreSQLPodcast EpisodeMongoDBAmazon AuroraPGCryptoPostGISpl/pgsqlForeign Data WrappersPipelineDBPodcast EpisodeCitusPodcast EpisodeJepsen TestingYugabyte Jepsen Test ResultsOLTP == Online Transaction ProcessingOLAP == Online Analytical ProcessingDocDBGoogle SpannerGoogle BigTableSpot InstancesKubernetesCloudformationTerraformPrometheusDebeziumPodcast EpisodeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary Databases are useful for inspecting the current state of your application, but inspecting the history of that data can get messy without a way to track changes as they happen. Debezium is an open source platform for reliable change data capture that you can use to build supplemental systems for everything from maintaining audit trails to real-time updates of your data warehouse. In this episode Gunnar Morling and Randall Hauch explain why it got started, how it works, and some of the myriad ways that you can use it. If you have ever struggled with implementing your own change data capture pipeline, or understanding when it would be useful then this episode is for you. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Randall Hauch and Gunnar Morling about Debezium, an open source distributed platform for change data capture Interview Introduction How did you get involved in the area of data management? Can you start by describing what Change Data Capture is and some of the ways that it can be used? What is Debezium and what problems does it solve? What was your motivation for creating it? What are some of the use cases that it enables? What are some of the other options on the market for handling change data capture? Can you describe the systems architecture of Debezium and how it has evolved since it was first created? How has the tight coupling with Kafka impacted the direction and capabilities of Debezium? What, if any, other substrates does Debezium support (e.g. Pulsar, Bookkeeper, Pravega)? What are the data sources that are supported by Debezium? Given that you have branched into non-relational stores, how have you approached organization of the code to allow for handling the specifics of those engines while retaining a common core set of functionality? What is involved in deploying, integrating, and maintaining an installation of Debezium? What are the scaling factors? What are some of the edge cases that users and operators should be aware of? Debezium handles the ingestion and distribution of database changesets. What are the downstream challenges or complications that application designers or systems architects have to deal with to make use of that information? What are some of the design tensions that exist in the Debezium community between acting as a simple pipe vs. adding functionality for interpreting/aggregating/formatting the information contained in the changesets? What are some of the common downstream systems that consume the outputs of Debezium? What challenges or complexities are involved in building clients that can consume the changesets from the different engines that you support? What are some of the most interesting, unexpected, or innovative ways that you have seen Debezium used? What have you found to be the most challenging, complex, or complicated aspects of building, maintaining, and growing Debezium? What is in store for the future of Debezium? Contact Info Randall LinkedIn @rhauch on Twitter rhauch on GitHub Gunnar gunnarmorling on GitHub @gunnarmorling on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Debezium Confluent Kafka Connect RedHat Bean Validation Change Data Capture DBMS == DataBase Management System Apache Kafka Apache Flink Podcast Episode Yugabyte DB PostgreSQL Podcast Episode MySQL Microsoft SQL Server Apache Pulsar Podcast Episode Pravega Podcast Episode NATS Amazon Kinesis Pulsar IO WePay The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how their teams are organized to allow for rapid growth and massive scale. Getting an inside look at the companies behind the services we use is always useful, and this conversation was no exception. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Vadim Semenov about how data engineers work at DataDog Interview Introduction How did you get involved in the area of data management? For anyone who isn’t familiar with DataDog, can you start by describing the types and volumes of data that you’re dealing with? What are the main components of your platform for managing that information? How are the data teams at DataDog organized and what are your primary responsibilities in the organization? What are some of the complexities and challenges that you face in your work as a result of the volume of data that you are processing? What are some of the strategies which have proven to be most useful in overcoming those challenges? Who are the main consumers of your work and how do you build in feedback cycles to ensure that their needs are being met? Given that the majority of the data being ingested by DataDog is timeseries, what are your lifecycle and retention policies for that information? Most of the data that you are working with is customer generated from your deployed agents and API integrations. How do you manage cleanliness and schema enforcement for the events as they are being delivered? What are some of the upcoming projects that you have planned for the upcoming months and years? What are some of the technologies, patterns, or practices that you are hoping to adopt? Contact Info LinkedIn @databuryat on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links DataDog Hadoop Hive Yarn Chef SRE == Site Reliability Engineer Application Performance Management (APM) Apache Kafka RocksDB Cassandra Apache Parquet data serialization format SLA == Service Level Agreement WatchDog Apache Spark Podcast Episode Apache Pig Databricks JVM == Java Virtual Machine Kubernetes SSIS (SQL Server Integration Services) Pentaho JasperSoft Apache Airflow Podcast.__init__ Episode Apache NiFi Podcast Episode Luigi Dagster Podcast Episode Prefect The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Transactional databases used in applications are optimized for fast reads and writes with relatively simple queries on a small number of records. Data warehouses are optimized for batched writes and complex analytical queries. Between those use cases there are varying levels of support for fast reads on quickly changing data. To address that need more completely the team at Materialize has created an engine that allows for building queryable views of your data as it is continually updated from the stream of changes being generated by your applications. In this episode Frank McSherry, chief scientist of Materialize, explains why it was created, what use cases it enables, and how it works to provide fast queries on continually updated data. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Frank McSherry about Materialize, an engine for maintaining materialized views on incrementally updated data from change data captures Interview Introduction How did you get involved in the area of data management? Can you start by describing what Materialize is and the problems that you are aiming to solve with it? What was your motivation for creating it? What use cases does Materialize enable? What are some of the existing tools or systems that you have seen employed to address those needs which can be replaced by Materialize? How does it fit into the broader ecosystem of data tools and platforms? What are some of the use cases that Materialize is uniquely able to support? How is Materialize architected and how has the design evolved since you first began working on it? Materialize is based on your timely-dataflow project, which itself is based on the work you did on Naiad. What was your reasoning for using Rust as the implementation target and what benefits has it provided? What are some of the components or primitives that were missing in the Rust ecosystem as compared to what is available in Java or C/C++, which have been the dominant languages for distributed data systems? In the list of features, you highlight full support for ANSI SQL 92. What were some of the edge cases that you faced in complying with that standard given the distributed execution context for Materialize? A majority of SQL oriented platforms define custom extensions or built-in functions that are specific to their problem domain. What are some of the existing or planned additions for Materialize? Can you talk through the lifecycle of data as it flows from the source database and through the Materialize engine? What are the considerations and constraints on maintaining the full history of the source data within Materialize? For someone who wants to use Materialize, what is involved in getting it set up and integrated with their data sources? What is the workflow for defining and maintaining a set of views? What are some of the complexities that users might face in ensuring the ongoing functionality of those views? For someone who is unfamiliar with the semantics of streaming SQL, what are some of the conceptual shifts that they should be aware of? The Materialize product is currently pre-release. What are the remaining steps before launching it? What do you have planned for the future of the product and company? Contact Info frankmcsherry on GitHub @frankmcsherry on Twitter Blog Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Materialize Timely Dataflow Dryad: Distributed Data-Parallel Programs from SequentialBuilding Blocks [Naiad](Programs from SequentialBuilding Blocks): A Timely Dataflow System Differential Privacy PageRank Data Council Presentation on Materialize Change Data Capture Debezium Apache Spark Podcast Episode Flink Podcast Episode Go language Rust Haskell Rust Borrow Checker GDB (GNU Debugger) Avro Apache Calcite ANSI SQL 92 Correlated Subqueries OOM (Out Of Memory) Killer Log-Structured Merge Tree The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools. At WeWork they needed a system that would provide visibility into their Airflow pipelines and the outputs produced. In this episode Julien Le Dem and Willy Lulciuc explain how they built Marquez to serve that need, how it is architected, and how it compares to other options that you might be considering. Even if you already have a metadata repository this is worth a listen to learn more about the value that visibility of your data can bring to your organization. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse? Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics. Their web based transformation tool with built in collaboration features lets your analysts own the full lifecycle of data in your warehouse. Featuring built in version control integration, real-time error checking for their SQL code, data quality tests, scheduling, and a data catalog with annotation capabilities it’s everything you need to keep your data warehouse in order. Sign up for a free trial today at dataengineeringpodcast.com/dataform and email [email protected] with the subject "Data Engineering Podcast" to get a hands-on demo from one of their data experts. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference, the Strata Data conference, and PyCon US. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Willy Lulciuc and Julien Le Dem about Marquez, an open source platform to collect, aggregate, and visualize a data ecosystem’s metadata Interview Introduction How did you get involved in the area of data management? Can you start by describing what Marquez is? What was missing in existing metadata management platforms that necessitated the creation of Marquez? How do the capabilities of Marquez compare with tools and services that bill themselves as data catalogs? How does it compare to the Amundsen platform that Lyft recently released? What are some of the tools or platforms that are currently integrated with Marquez and what additional integrations would you like to see? What are some of the capabilities that are unique to Marquez and how are you using them at WeWork? What are the primary resource types that you support in Marquez? What are some of the lowest common denominator attributes that are necessary and useful to track in a metadata repository? Can you explain how Marquez is architected and how the design has evolved since you first began working on it? Many metadata management systems are simply a service layer on top of a separate data storage engine. What are the benefits of using PostgreSQL as the system of record for Marquez? What are some of the complexities that arise from relying on a relational engine as opposed to a document store or graph database? How is the metadata itself stored and managed in Marquez? How much up-front data modeling is necessary and what types of schema representations are supported? Can you talk through the overall workflow of someone using Marquez in their environment? What is involved in registering and updating datasets? How do you define and track the health of a given dataset? What are some of the interesting questions that can be answered from the information stored in Marquez? What were your assumptions going into this project and how have they been challenged or updated as you began using it for production use cases? For someone who is interested in using Marquez what is involved in deploying and maintaining an installation of it? What have you found to be the most challenging or unanticipated aspects of building and maintaining a metadata repository and data discovery platform? When is Marquez the wrong choice for a metadata repository? What do you have planned for the future of Marquez? Contact Info Julien Le Dem @J_ on Twitter Email julienledem on GitHub Willy LinkedIn @wslulciuc on Twitter wslulciuc on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Marquez DataEngConf Presentation WeWork Canary Yahoo Dremio Hadoop Pig Parquet Podcast Episode Airflow Apache Atlas Amundsen Podcast Episode Uber DataBook LinkedIn DataHub Iceberg Table Format Podcast Episode Delta Lake Podcast Episode Great Expectations data pipeline unit testing framework Podcast.__init__ Episode Redshift SnowflakeDB Podcast Episode Apache Kafka Schema Registry Podcast Episode Open Tracing Jaeger Zipkin DropWizard Java framework Marquez UI Cayley Graph Database Kubernetes Marquez Helm Chart Marquez Docker Container Dagster Podcast Episode Luigi DBT Podcast Episode Thrift Protocol Buffers The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditional data warehouse engines, the features that allow you to scale your usage dynamically, and how it allows for a shift in your workflow from ETL to ELT. If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCOn US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Kent Graziano about SnowflakeDB, the cloud-native data warehouse Interview Introduction How did you get involved in the area of data management? Can you start by explaining what SnowflakeDB is for anyone who isn’t familiar with it? How does it compare to the other available platforms for data warehousing? How does it differ from traditional data warehouses? How does the performance and flexibility affect the data modeling requirements? Snowflake is one of the data stores that is enabling the shift from an ETL to an ELT workflow. What are the features that allow for that approach and what are some of the challenges that it introduces? Can you describe how the platform is architected and some of the ways that it has evolved as it has grown in popularity? What are some of the current limitations that you are struggling with? For someone getting started with Snowflake what is involved with loading data into the platform? What is their workflow for allocating and scaling compute capacity and running anlyses? One of the interesting features enabled by your architecture is data sharing. What are some of the most interesting or unexpected uses of that capability that you have seen? What are some other features or use cases for Snowflake that are not as well known or publicized which you think users should know about? When is SnowflakeDB the wrong choice? What are some of the plans for the future of SnowflakeDB? Contact Info LinkedIn Website @KentGraziano on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links SnowflakeDB Free Trial Stack Overflow Data Warehouse Oracle DB MPP == Massively Parallel Processing Shared Nothing Architecture Multi-Cluster Shared Data Architecture Google BigQuery AWS Redshift AWS Redshift Spectrum Presto Podcast Episode SnowflakeDB Semi-Structured Data Types Hive ACID == Atomicity, Consistency, Isolation, Durability 3rd Normal Form Data Vault Modeling Dimensional Modeling JSON AVRO Parquet SnowflakeDB Virtual Warehouses CRM == Customer Relationship Management Master Data Management Podcast Episode FoundationDB Podcast Episode Apache Spark Podcast Episode SSIS == SQL Server Integration Services Talend Informatica Fivetran Podcast Episode Matillion Apache Kafka Snowpipe Snowflake Data Exchange OLTP == Online Transaction Processing GeoJSON Snowflake Documentation SnowAlert Splunk Data Catalog The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The financial industry has long been driven by data, requiring a mature and robust capacity for discovering and integrating valuable sources of information. Citadel is no exception, and in this episode Michael Watson and Robert Krzyzanowski share their experiences managing and leading the data engineering teams that power the business. They shared helpful insights into some of the challenges associated with working in a regulated industry, organizing teams to deliver value rapidly and reliably, and how they approach career development for data engineers. This was a great conversation for an inside look at how to build and maintain a data driven culture. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Michael Watson and Robert Krzyzanowski about the technical and organizational challenges that he and his team are working on at Citadel Interview Introduction How did you get involved in the area of data management? Can you start by describing the size and structure of the data engineering teams at Citadel? How have the scope and nature of responsibilities for data engineers evolved over the past few years at Citadel as more and better tools and platforms have been made available in the space and machine learning techniques have grown more sophisticated? Can you describe the types of data that you are working with at Citadel? What is the process for identifying, evaluating, and ingesting new sources of data? What are some of the common core aspects of your data infrastructure? What are some of the ways that it differs across teams or projects? How involved are data engineers in the overall product design and delivery lifecycle? For someone who joins your team as a data engineer, what are some of the options available to them for a career path? What are some of the challenges that you are currently facing in managing the data lifecycle for projects at Citadel? What are some tools or practices that you are excited to try out? Contact Info Michael LinkedIn @detroitcoder on Twitter detroitcoder on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Citadel Python Hedge Fund Quantitative Trading Citadel Securities Apache Airflow Jupyter Hub Alembic database migrations for SQLAlchemy Terraform DQM == Data Quality Management Great Expectations Podcast.__init__ Episode Nomad RStudio Active Directory The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The team at Sentry has built a platform for anyone in the world to send software errors and events. As they scaled the volume of customers and data they began running into the limitations of their initial architecture. To address the needs of their business and continue to improve their capabilities they settled on Clickhouse as the new storage and query layer to power their business. In this episode James Cunningham and Ted Kaemming describe the process of rearchitecting a production system, what they learned in the process, and some useful tips for anyone else evaluating Clickhouse. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Ted Kaemming and James Cunningham about Snuba, the new open source search service at Sentry implemented on top of Clickhouse Interview Introduction How did you get involved in the area of data management? Can you start by describing the internal and user-facing issues that you were facing at Sentry with the existing search capabilities? What did the previous system look like? What was your design criteria for building a new platform? What was your initial list of possible system components and what was your evaluation process that resulted in your selection of Clickhouse? Can you describe the system architecture of Snuba and some of the ways that it differs from your initial ideas of how it would work? What have been some of the sharp edges of Clickhouse that you have had to engineer around? How have you found the operational aspects of Clickhouse? How did you manage the introduction of this new piece of infrastructure to a business that was already handling massive amounts of real-time data? What are some of the downstream benefits of using Clickhouse for managing event data at Sentry? For someone who is interested in using Snuba for their own purposes, how flexible is it for different domain contexts? What are some of the other data challenges that you are currently facing at Sentry? What is your next highest priority for evolving or rebuilding to address technical or business challenges? Contact Info James @JTCunning on Twitter JTCunning on GitHub Ted tkaemming on GitHub Website @tkaemming on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Sentry Podcast.__init__ Episode Snuba Blog Post Clickhouse Podcast Episode Disqus Urban Airship HBase Google Bigtable PostgreSQL Redis HyperLogLog Riak Celery RabbitMQ Apache Spark Presto Cassandra Apache Kudu Apache Pinot Apache Druid Flask Apache Kafka Cassandra Tombstone Sentry Blog XML Change Data Capture The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary With the constant evolution of technology for data management it can seem impossible to make an informed decision about whether to build a data warehouse, or a data lake, or just leave your data wherever it currently rests. What’s worse is that any time you have to migrate to a new architecture, all of your analytical code has to change too. Thankfully it’s possible to add an abstraction layer to eliminate the churn in your client code, allowing you to evolve your data platform without disrupting your downstream data users. In this episode AtScale co-founder and CTO Matthew Baird describes how the data virtualization and data engineering automation capabilities that are built into the platform free up your engineers to focus on your business needs without having to waste cycles on premature optimization. This was a great conversation about the power of abstractions and appreciating the value of increasing the efficiency of your data team. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more. Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at dataengineeringpodcast.com/chaossearch and don’t forget to thank them for supporting the show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Matt Baird about AtScale, a platform that Interview Introduction How did you get involved in the area of data management? Can you start by describing the AtScale platform and how it fits in the ecosystem of data tools? What was your motivation for building the platform and what were some of the early challenges that you faced in achieving your current level of success? How is the AtScale platform architected and what have been some of the main areas of evolution and change since you first began building it? How has the surrounding data ecosystem changed since AtScale was founded? How are current industry trends influencing your product focus? Can you talk through the workflow for someone implementing AtScale? What are some of the main use cases that benefit from data virtualization capabilities? How does it influence the relevancy of data warehouses or data lakes? What are some of the types of tools or patterns that AtScale replaces in a data platform? What are some of the most interesting or unexpected ways that you have seen AtScale used? What have been some of the most challenging aspects of building and growing the platform? When is AtScale the wrong choice? What do you have planned for the future of the platform and business? Contact Info LinkedIn @zetty on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links AtScale PeopleSoft Oracle Hadoop PrestoDB Impala Apache Kylin Apache Druid Go Language Scala The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The practice of data management is one that requires technical acumen, but there are also many policy and regulatory issues that inform and influence the design of our systems. With the introduction of legal frameworks such as the EU GDPR and California’s CCPA it is necessary to consider how to implement data protectino and data privacy principles in the technical and policy controls that govern our data platforms. In this episode Karen Heaton and Mark Sherwood-Edwards share their experience and expertise in helping organizations achieve compliance. Even if you aren’t subject to specific rules regarding data protection it is definitely worth listening to get an overview of what you should be thinking about while building and running data pipelines. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more. Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at dataengineeringpodcast.com/chaossearch and don’t forget to thank them for supporting the show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Karen Heaton and Mark Sherwood-Edwards about the idea of data protection, why you might need it, and how to include the principles in your data pipelines. Interview Introduction How did you get involved in the area of data management? Can you start by explaining what is encompassed by the idea of data protection? What regulations control the enforcement of data protection requirements, and how can we determine whether we are subject to their rules? What are some of the conflicts and constraints that act against our efforts to implement data protection? How much of data protection is handled through technical implementation as compared to organizational policies and reporting requirements? Can you give some examples of the types of information that are subject to data protection? One of the challenges in data management generally is tracking the presence and usage of any given information. What are some strategies that you have found effective for auditing the usage of protected information? A corollary to tracking and auditing of protected data in the GDPR is the need to allow for deletion of an individual’s information. How can we ensure effective deletion of these records when dealing with multiple storage systems? What are some of the system components that are most helpful in implementing and maintaining technical and policy controls for data protection? How do data protection regulations impact or restrict the technology choices that are viable for the data preparation layer? Who in the organization is responsible for the proper compliance to GDPR and other data protection regimes? Downstream from the storage and management platforms that we build as data engineers are data scientists and analysts who might request access to protected information. How do the regulations impact the types of analytics that they can use? Contact Info Karen Email Website Mark Email Website GDPR Now Podcast Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Data Protection GDPR This Is DPO Intellectual Property European Convention Of Human Rights CCPA == California Consumer Privacy Act PII == Personally Identifiable Information Privacy By Design US Privacy Shield Principle of Least Privilege International Association of Privacy Professionals Privacy Technology Vendor Report Data Provenance Chief Data Officer UK ICO (Information Commissioner’s Office) AI Audit Framework Data Council The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary As data engineers the health of our pipelines is our highest priority. Unfortunately, there are countless ways that our dataflows can break or degrade that have nothing to do with the business logic or data transformations that we write and maintain. Sean Knapp founded Ascend to address the operational challenges of running a production grade and scalable Spark infrastructure, allowing data engineers to focus on the problems that power their business. In this episode he explains the technical implementation of the Ascend platform, the challenges that he has faced in the process, and how you can use it to simplify your dataflow automation. This is a great conversation to get an understanding of all of the incidental engineering that is necessary to make your data reliable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com today to find out more. Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at dataengineeringpodcast.com/chaossearch and don’t forget to thank them for supporting the show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Sean Knapp about Ascend, which he is billing as an autonomous dataflow service Interview Introduction How did you get involved in the area of data management? Can you start by explaining what the Ascend platform is? What was your inspiration for creating it and what keeps you motivated? What was your criteria for determining the best execution substrate for the Ascend platform? Can you describe any limitations that are imposed by your selection of Spark as the processing engine? If you were to rewrite Spark from scratch today to fit your particular requirements, what would you change about it? Can you describe the technical implementation of Ascend? How has the system design evolved since you first began working on it? What are some of the assumptions that you had at the beginning of your work on Ascend that have been challenged or updated as a result of working with the technology and your customers? How does the programming interface for Ascend differ from that of a vanilla Spark deployment? What are the main benefits that a data engineer would get from using Ascend in place of running their own Spark deployment? How do you enforce the lack of side effects in the transforms that comprise the dataflow? Can you describe the pipeline orchestration system that you have built into Ascend and the benefits that it provides to data engineers? What are some of the most challenging aspects of building and launching Ascend that you have dealt with? What are some of the most interesting or unexpected lessons learned or edge cases that you have encountered? What are some of the capabilities that you are most proud of and which have gained the greatest adoption? What are some of the sharp edges that remain in the platform? When is Ascend the wrong choice? What do you have planned for the future of Ascend? Contact Info LinkedIn @seanknapp on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Ascend Kubernetes BigQuery Apache Spark Apache Beam Go Language SHA Hashes PySpark Delta Lake Podcast Episode DAG == Directed Acyclic Graph PrestoDB MinIO Podcast Episode Parquet Snappy Compression Tensorflow Kafka Druid The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Despite the fact that businesses have relied on useful and accurate data to succeed for decades now, the state of the art for obtaining and maintaining that information still leaves much to be desired. In an effort to create a better abstraction for building data applications Nick Schrock created Dagster. In this episode he explains his motivation for creating a product for data management, how the programming model simplifies the work of building testable and maintainable pipelines, and his vision for the future of data programming. If you are building dataflows then Dagster is definitely worth exploring. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Nick Schrock about Dagster, an open source system for building modern data applications Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Dagster is and the origin story for the project? In the tagline for Dagster you describe it as "a system for building modern data applications". There are a lot of contending terms that one might use in this context, such as ETL, data pipelines, etc. Can you describe your thinking as to what the term "data application" means, and the types of use cases that Dagster is well suited for? Can you talk through how Dagster is architected and some of the ways that it has evolved since you first began working on it? What do you see as the current industry trends that are leading us away from full stack frameworks such as Airflow and Oozie for ETL and into an abstracted programming environment that is composable with different execution contexts? What are some of the initial assumptions that you had which have been challenged or updated in the process of working with users of Dagster? For someone who wants to extend Dagster, or integrate it with other components of their data infrastructure, such as a metadata engine, what interfaces do you provide for extensibility? For someone who wants to get started with Dagster can you describe a typical workflow for writing a data pipeline? Once they have something working, what is involved in deploying it? One of the things that stands out about Dagster is the strong contracts that it enforces between computation nodes, or "solids". Why do you feel that those contracts are necessary, and what benefits do they provide during the full lifecycle of a data application? Another difficult aspect of data applications is testing, both before and after deploying it to a production environment. How does Dagster help in that regard? It is also challenging to keep track of the entirety of a DAG for a given workflow. How does Dagit keep track of the task dependencies, and what are the limitations of that tool? Can you give an overview of where you see Dagster fitting in the overall ecosystem of data tools? What are some of the features or capabilities of Dagster which are often overlooked that you would like to highlight for the listeners? Your recent release of Dagster includes a built-in scheduler, as well as a built-in deployment capability. Why did you feel that those were necessary capabilities to incorporate, rather than continuing to leave that as end-user considerations? You have built a new company around Dagster in the form of Elementl. How are you approaching sustainability and governance of Dagster, and what is your path to sustainability for the business? What should listeners be keeping an eye out for in the near to medium future from Elementl and Dagster? What is on your roadmap that you consider necessary before creating a 1.0 release? Contact Info @schrockn on Twitter schrockn on GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Dagster Elementl ETL GraphQL React Matei Zaharia DataOps Episode Kafka Fivetran Podcast Episode Spark Supervised Learning DevOps Luigi Airflow Dask Podcast Episode Kubernetes Ray Maxime Beauchemin Podcast Interview Dagster Testing Guide Great Expectations Podcast.__init__ Interview Papermill Notebooks At Netflix Episode DBT Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The scale and complexity of the systems that we build to satisfy business requirements is increasing as the available tools become more sophisticated. In order to bridge the gap between legacy infrastructure and evolving use cases it is necessary to create a unifying set of components. In this episode Dipti Borkar explains how the emerging category of data orchestration tools fills this need, some of the existing projects that fit in this space, and some of the ways that they can work together to simplify projects such as cloud migration and hybrid cloud environments. It is always useful to get a broad view of new trends in the industry and this was a helpful perspective on the need to provide mechanisms to decouple physical storage from computing capacity. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Dipti Borkark about data orchestration and how it helps in migrating data workloads to the cloud Interview Introduction How did you get involved in the area of data management? Can you start by describing what you mean by the term "Data Orchestration"? How does it compare to the concept of "Data Virtualization"? What are some of the tools and platforms that fit under that umbrella? What are some of the motivations for organizations to use the cloud for their data oriented workloads? What are they giving up by using cloud resources in place of on-premises compute? For businesses that have invested heavily in their own datacenters, what are some ways that they can begin to replicate some of the benefits of cloud environments? What are some of the common patterns for cloud migration projects and what challenges do they present? Do you have advice on useful metrics to track for determining project completion or success criteria? How do businesses approach employee education for designing and implementing effective systems for achieving their migration goals? Can you talk through some of the ways that different data orchestration tools can be composed together for a cloud migration effort? What are some of the common pain points that organizations encounter when working on hybrid implementations? What are some of the missing pieces in the data orchestration landscape? Are there any efforts that you are aware of that are aiming to fill those gaps? Where is the data orchestration market heading, and what are some industry trends that are driving it? What projects are you most interested in or excited by? For someone who wants to learn more about data orchestration and the benefits the technologies can provide, what are some resources that you would recommend? Contact Info LinkedIn @dborkar on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Alluxio Podcast Episode UC San Diego Couchbase Presto Podcast Episode Spark SQL Data Orchestration Data Virtualization PyTorch Podcast.init Episode Rook storage orchestration PySpark MinIO Podcast Episode Kubernetes Openstack Hadoop HDFS Parquet Files Podcast Episode ORC Files Hive Metastore Iceberg Table Format Podcast Episode Data Orchestration Summit Star Schema Snowflake Schema Data Warehouse Data Lake Teradata The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. Dataform is a platform that helps you apply engineering principles to your data transformations and table definitions, including unit testing SQL scripts, defining repeatable pipelines, and adding metadata to your warehouse to improve your team’s communication. In this episode CTO and co-founder of Dataform Lewis Hemens joins the show to explain his motivation for creating the platform and company, how it works under the covers, and how you can start using it today to get your data warehouse under control. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral. They provide an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure. Datacoral’s customers report that their data engineers are able to spend 80% of their work time invested in data transformations, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from mere terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit Datacoral.com today to find out more. Are you working on data, analytics, or AI using platforms such as Presto, Spark, or Tensorflow? Check out the Data Orchestration Summit on November 7 at the Computer History Museum in Mountain View. This one day conference is focused on the key data engineering challenges and solutions around building analytics and AI platforms. Attendees will hear from companies including Walmart, Netflix, Google, and DBS Bank on how they leveraged technologies such as Alluxio, Presto, Spark, Tensorflow, and you will also hear from creators of open source projects including Alluxio, Presto, Airflow, Iceberg, and more! Use discount code PODCAST for 25% off of your ticket, and the first five people to register get free tickets! Register now as early bird tickets are ending this week! Attendees will takeaway learnings, swag, a free voucher to visit the museum, and a chance to win the latest ipad Pro! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Lewis Hemens about DataForm, a platform that helps analysts manage all data processes in your cloud data warehouse Interview Introduction How did you get involved in the area of data management? Can you start by explaining what DataForm is and the origin story for the platform and company? What are the main benefits of using a tool like DataForm and who are the primary users? Can you talk through the workflow for someone using DataForm and highlight the main features that it provides? What are some of the challenges and mistakes that are common among engineers and analysts with regard to versioning and evolving schemas and the accompanying data? How does CI/CD and change management manifest in the context of data warehouse management? How is the Dataform SDK itself implemented and how has it evolved since you first began working on it? Can you differentiate the capabilities between the open source CLI and the hosted web platform, and when you might need to use one over the other? What was your selection process for an embedded runtime and how did you decide on javascript? Can you talk through some of the use cases that having an embedded runtime enables? What are the limitations of SQL when working in a collaborative environment? Which database engines do you support and how do you reduce the maintenance burden for supporting different dialects and capabilities? What is involved in adding support for a new backend? When is DataForm the wrong choice? What do you have planned for the future of DataForm? Contact Info LinkedIn @lewishemens on Twitter lewish on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links DataForm YCombinator DBT == Data Build Tool Podcast Episode Fishtown Analytics Typescript Continuous Integration Continuous Delivery BigQuery Snowflake DB UDF == User Defined Function RedShift PostgreSQL Podcast Episode AWS Athena Presto Podcast Episode Apache Beam Apache Kafka Segment Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The process of exposing your data through a SQL interface has many possible pathways, each with their own complications and tradeoffs. One of the recent options is Rockset, a serverless platform for fast SQL analytics on semi-structured and structured data. In this episode CEO Venkat Venkataramani and SVP of Product Shruti Bhat explain the origins of Rockset, how it is architected to allow for fast and flexible SQL analytics on your data, and how their serverless platform can save you the time and effort of implementing portions of your own infrastructure. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral. They provide an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure. Datacoral’s customers report that their data engineers are able to spend 80% of their work time invested in data transformations, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from mere terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit Datacoral.com today to find out more. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Shruti Bhat and Venkat Venkataramani about Rockset, a serverless platform for enabling fast SQL queries across all of your data Interview Introduction How did you get involved in the area of data management? Can you start by describing what Rockset is and your motivation for creating it? What are some of the use cases that it enables which would otherwise be impractical or intractable? How does Rockset fit into the infrastructure and workflow of data teams and what portions of a typical stack does it replace? Can you describe how the Rockset platform is architected and how it has evolved as you onboard more customers? Can you describe the flow of a piece of data as it traverses the full lifecycle in Rockset? How is your storage backend implemented to allow for speed and flexibility in the query layer? How does it manage distribution, balancing, and durability of the data? What are your strategies for handling node and region failure in the cloud? You have a whitepaper describing your architecture as being oriented around microservices on Kubernetes in order to be cloud agnostic. How do you handle the case where customers have data sources that span multiple cloud providers or regions and the latency that can result? How is the query engine structured to allow for optimizing so many different query types (e.g. search, graph, timeseries, etc.)? With Rockset handling a large portion of the underlying infrastructure work that a data engineer might be involved with, what are some ways that you have seen them use the time that they have gained and how has that benefitted the organizations that they work for? What are some of the most interesting/unexpected/innovative ways that you have seen Rockset used? When is Rockset the wrong choice for a given project? What have you found to be the most challenging and the most exciting aspects of building the Rockset platform and company? What do you have planned for the future of Rockset? Contact Info Venkat LinkedIn @iamveeve on Twitter veeve on GitHub Shruti LinkedIn @shrutibhat on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at pythonpodcast.com/chat Links Rockset Blog Oracle VMWare Facebook Rube Goldberg Machine SnowflakeDB Protocol Buffers Spark Podcast Episode Presto Podcast Episode Apache Kafka RocksDB InnoDB Lucene Log Structured Merge Tree (LSTM) Kubernetes The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Building an end-to-end data pipeline for your machine learning projects is a complex task, made more difficult by the variety of ways that you can structure it. Kedro is a framework that provides an opinionated workflow that lets you focus on the parts that matter, so that you don’t waste time on gluing the steps together. In this episode Tom Goldenberg explains how it works, how it is being used at Quantum Black for customer projects, and how it can help you structure your own. Definitely worth a listen to gain more understanding of the benefits that a standardized process can provide. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, Data Council in Barcelona, and the Data Orchestration Summit. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Tom Goldenberg about Kedro, an open source development workflow tool that helps structure reproducible, scaleable, deployable, robust and versioned data pipelines. Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Kedro is and its origin story? Who are the primary users of Kedro, and how does it fit into and impact the workflow of data engineers and data scientists? Can you talk through a typical lifecycle for a project that is built using Kedro? What are the overall features of Kedro and how do they compound to encourage best practices for data projects? How does the culture and background of QuantumBlack influence the design and capabilities of Kedro? What was the motivation for releasing it publicly as an open source framework? What are some examples of ways that Kedro is being used within QuantumBlack and how has that experience informed the design and direction of the project? Can you describe how Kedro itself is implemented and how it has evolved since you first started working on it? There has been a recent trend away from end-to-end ETL frameworks and toward a decoupled model that focuses on a programming target with pluggable execution. What are the industry pressures that are driving that shift and what are your thoughts on how that will manifest in the long term? How do the capabilities and focus of Kedro compare to similar projects such as Prefect and Dagster? It has not yet reached a stable release. What are the aspects of Kedro that are still in flux and where are the changes most concentrated? What is still missing for a stable 1.x release? What are some of the most interesting/innovative/unexpected ways that you have seen Kedro used? When is Kedro the wrong choice? What do you have in store for the future of Kedro? Contact Info LinkedIn @tomgoldenberg on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Kedro GitHub Quantum Black Labs GitHub Agolo McKinsey Airflow Docker Kubernetes DataBricks Formula 1 Kedro Viz Dask Podcast Interview Py.Test Azure Data Factory Prefect Podcast Interview Dagster The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Object storage is quickly becoming the unifying layer for data intensive applications and analytics. Modern, cloud oriented data warehouses and data lakes both rely on the durability and ease of use that it provides. S3 from Amazon has quickly become the de-facto API for interacting with this service, so the team at MinIO have built a production grade, easy to manage storage engine that replicates that interface. In this episode Anand Babu Periasamy shares the origin story for the MinIO platform, the myriad use cases that it supports, and the challenges that they have faced in replicating the functionality of S3. He also explains the technical implementation, innovative design, and broad vision for the project. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Anand Babu Periasamy about MinIO, the neutral, open source, enterprise grade object storage system. Interview Introduction How did you get involved in the area of data management? Can you explain what MinIO is and its origin story? What are some of the main use cases that MinIO enables? How does MinIO compare to other object storage options and what benefits does it provide over other open source platforms? Your marketing focuses on the utility of MinIO for ML and AI workloads. What benefits does object storage provide as compared to distributed file systems? (e.g. HDFS, GlusterFS, Ceph) What are some of the challenges that you face in terms of maintaining compatibility with the S3 interface? What are the constraints and opportunities that are provided by adhering to that API? Can you describe how MinIO is implemented and the overall system design? How has that design evolved since you first began working on it? What assumptions did you have at the outset and how have they been challenged or updated? What are the axes for scaling that MinIO provides and how does it handle clustering? Where does it fall on the axes of availability and consistency in the CAP theorem? One of the useful features that you provide is efficient erasure coding, as well as protection against data corruption. How much overhead do those capabilties incur, in terms of computational efficiency and, in a clustered scenario, storage volume? For someone who is interested in running MinIO, what is involved in deploying and maintaining an installation of it? What are the cases where it makes sense to use MinIO in place of a cloud-native object store such as S3 or Google Cloud Storage? How do you approach project governance and sustainability? What are some of the most interesting/innovative/unexpected ways that you have seen MinIO used? What do you have planned for the future of MinIO? Contact Info LinkedIn @abperiasamy on Twitter abperiasamy on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links MinIO GlusterFS Object Storage RedHat Bionics AWS S3 Ceph Swift Stack POSIX HDFS Google BigQuery AzureML AWS SageMaker AWS Athena S3 Select Azure Blob Store BackBlaze Round Robin DNS Service Mesh Istio Envoy SmartStack Free Software RocksDB TanTan Blog Post Presto SparkML MCAdmin Trace DTrace The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast