Data Engineering Podcast

Tobias Macey
Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

  1. 5 DAYS AGO

    Accelerate Migration Of Your Data Warehouse with Datafold's AI Powered Migration Agent

    Summary Gleb Mezhanskiy, CEO and co-founder of DataFold, joins Tobias Macey to discuss the challenges and innovations in data migrations. Gleb shares his experiences building and scaling data platforms at companies like Autodesk and Lyft, and how these experiences inspired the creation of DataFold to address data quality issues across teams. He outlines the complexities of data migrations, including common pitfalls such as technical debt and the importance of achieving parity between old and new systems. Gleb also discusses DataFold's innovative use of AI and large language models (LLMs) to automate translation and reconciliation processes in data migrations, reducing time and effort required for migrations. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementImagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!Your host is Tobias Macey and today I'm welcoming back Gleb Mezhanskiy to talk about Datafold's experience bringing AI to bear on the problem of migrating your data stackInterview IntroductionHow did you get involved in the area of data management?Can you describe what the Data Migration Agent is and the story behind it?What is the core problem that you are targeting with the agent?What are the biggest time sinks in the process of database and tooling migration that teams run into?Can you describe the architecture of your agent?What was your selection and evaluation process for the LLM that you are using?What were some of the main unknowns that you had to discover going into the project?What are some of the evolutions in the ecosystem that occurred either during the development process or since your initial launch that have caused you to second-guess elements of the design?In terms of SQL translation there are libraries such as SQLGlot and the work being done with SDF that aim to address that through AST parsing and subsequent dialect generation. What are the ways that approach is insufficient in the context of a platform migration?How does the approach you are taking with the combination of data-diffing and automated translation help build confidence in the migration target?What are the most interesting, innovative, or unexpected ways that you have seen the Data Migration Agent used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI powered migration assistant?When is the data migration agent the wrong choice?What do you have planned for the future of applications of AI at Datafold?Contact Info LinkedInParting 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.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 hosts@dataengineeringpodcast.com with your story.Links DatafoldDatafold Migration AgentDatafold data-diffDatafold Reconciliation Podcast EpisodeSQLGlotLark parserClaude 3.5 SonnetLookerPodcast EpisodeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

    49 min
  2. OCT 20

    Bring Vector Search And Storage To The Data Lake With Lance

    Summary The rapid growth of generative AI applications has prompted a surge of investment in vector databases. While there are numerous engines available now, Lance is designed to integrate with data lake and lakehouse architectures. In this episode Weston Pace explains the inner workings of the Lance format for table definitions and file storage, and the optimizations that they have made to allow for fast random access and efficient schema evolution. In addition to integrating well with data lakes, Lance is also a first-class participant in the Arrow ecosystem, making it easy to use with your existing ML and AI toolchains. This is a fascinating conversation about a technology that is focused on expanding the range of options for working with vector data. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementImagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!Your host is Tobias Macey and today I'm interviewing Weston Pace about the Lance file and table format for column-oriented vector storageInterview IntroductionHow did you get involved in the area of data management?Can you describe what Lance is and the story behind it?What are the core problems that Lance is designed to solve?What is explicitly out of scope?The README mentions that it is straightforward to convert to Lance from Parquet. What is the motivation for this compatibility/conversion support?What formats does Lance replace or obviate?In terms of data modeling Lance obviously adds a vector type, what are the features and constraints that engineers should be aware of when modeling their embeddings or arbitrary vectors?Are there any practical or hard limitations on vector dimensionality?When generating Lance files/datasets, what are some considerations to be aware of for balancing file/chunk sizes for I/O efficiency and random access in cloud storage?I noticed that the file specification has space for feature flags. How has that aided in enabling experimentation in new capabilities and optimizations?What are some of the engineering and design decisions that were most challenging and/or had the biggest impact on the performance and utility of Lance?The most obvious interface for reading and writing Lance files is through LanceDB. Can you describe the use cases that it focuses on and its notable features?What are the other main integrations for Lance?What are the opportunities or roadblocks in adding support for Lance and vector storage/indexes in e.g. Iceberg or Delta to enable its use in data lake environments?What are the most interesting, innovative, or unexpected ways that you have seen Lance used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Lance format?When is Lance the wrong choice?What do you have planned for the future of Lance?Contact Info LinkedInGitHubParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Links Lance FormatLanceDBSubstraitPyArrowFAISSPineconePodcast EpisodeParquetIcebergPodcast EpisodeDelta LakePodcast EpisodePyLanceHilbert CurvesSIFT VectorsS3 ExpressWekaDataFusionRay DataTorch Data LoaderHNSW == Hierarchical Navigable Small Worlds vector indexIVFPQ vector indexGeoJSONPolarsThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

    58 min
  3. OCT 13

    The Role of Python in Shaping the Future of Data Platforms with DLT

    Summary In this episode of the Data Engineering Podcast, Adrian Broderieux and Marcin Rudolph, co-founders of DLT Hub, delve into the principles guiding DLT's development, emphasizing its role as a library rather than a platform, and its integration with lakehouse architectures and AI application frameworks. The episode explores the impact of the Python ecosystem's growth on DLT, highlighting integrations with high-performance libraries and the benefits of Arrow and DuckDB. The episode concludes with a discussion on the future of DLT, including plans for a portable data lake and the importance of interoperability in data management tools. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementImagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!Your host is Tobias Macey and today I'm interviewing Adrian Brudaru and Marcin Rudolf, cofounders at dltHub, about the growth of dlt and the numerous ways that you can use it to address the complexities of data integrationInterview IntroductionHow did you get involved in the area of data management?Can you describe what dlt is and how it has evolved since we last spoke (September 2023)?What are the core principles that guide your work on dlt and dlthub?You have taken a very opinionated stance against managed extract/load services. What are the shortcomings of those platforms, and when would you argue in their favor?The landscape of data movement has undergone some interesting changes over the past year. Most notably, the growth of PyAirbyte and the rapid shifts around the needs of generative AI stacks (vector stores, unstructured data processing, etc.). How has that informed your product development and positioning?The Python ecosystem, and in particular data-oriented Python, has also undergone substantial evolution. What are the developments in the libraries and frameworks that you have been able to benefit from?What are some of the notable investments that you have made in the developer experience for building dlt pipelines?How have the interfaces for source/destination development improved?You recently published a post about the idea of a portable data lake. What are the missing pieces that would make that possible, and what are the developments/technologies that put that idea within reach?What is your strategy for building a sustainable product on top of dlt?How does that strategy help to form a "virtuous cycle" of improving the open source foundation?What are the most interesting, innovative, or unexpected ways that you have seen dlt used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on dlt?When is dlt the wrong choice?What do you have planned for the future of dlt/dlthub?Contact Info AdrianLinkedInMarcinLinkedInParting 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.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 hosts@dataengineeringpodcast.com with your story.Links dltPod

    54 min
  4. OCT 6

    Build Your Data Transformations Faster And Safer With SDF

    Summary In this episode of the Data Engineering Podcast Lukas Schulte, co-founder and CEO of SDF, explores the development and capabilities of this fast and expressive SQL transformation tool. From its origins as a solution for addressing data privacy, governance, and quality concerns in modern data management, to its unique features like static analysis and type correctness, Lucas dives into what sets SDF apart from other tools like DBT and SQL Mesh. Tune in for insights on building a business around a developer tool, the importance of community and user experience in the data engineering ecosystem, and plans for future development, including supporting Python models and enhancing execution capabilities. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementImagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!Your host is Tobias Macey and today I'm interviewing Lukas Schulte about SDF, a fast and expressive SQL transformation tool that understands your schemaInterview IntroductionHow did you get involved in the area of data management?Can you describe what SDF is and the story behind it?What's the story behind the name?What problem are you solving with SDF?dbt has been the dominant player for SQL-based transformations for several years, with other notable competition in the form of SQLMesh. Can you give an overview of the venn diagram for features and functionality across SDF, dbt and SQLMesh?Can you describe the design and implementation of SDF?How have the scope and goals of the project changed since you first started working on it?What does the development experience look like for a team working with SDF?How does that differ between the open and paid versions of the product?What are the features and functionality that SDF offers to address intra- and inter-team collaboration?One of the challenges for any second-mover technology with an established competitor is the adoption/migration path for teams who have already invested in the incumbent (dbt in this case). How are you addressing that barrier for SDF?Beyond the core migration path of the direct functionality of the incumbent product is the amount of tooling and communal knowledge that grows up around that product. How are you thinking about that aspect of the current landscape?What is your governing principle for what capabilities are in the open core and which go in the paid product?What are the most interesting, innovative, or unexpected ways that you have seen SDF used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on SDF?When is SDF the wrong choice?What do you have planned for the future of SDF?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Links SDFSemantic Data Warehouseasdf-vmdbtSoftware Linting)SQLMeshPodcast EpisodeCoalescePodcast EpisodeApache IcebergPodcast EpisodeDuckDB Podcast Episode SDF Classifiersdbt Semantic Layerdbt expectationsApache DatafusionIbisThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

    43 min
  5. SEP 23

    Scaling Airbyte: Challenges and Milestones on the Road to 1.0

    Summary Airbyte is one of the most prominent platforms for data movement. Over the past 4 years they have invested heavily in solutions for scaling the self-hosted and cloud operations, as well as the quality and stability of their connectors. As a result of that hard work, they have declared their commitment to the future of the platform with a 1.0 release. In this episode Michel Tricot shares the highlights of their journey and the exciting new capabilities that are coming next. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementYour host is Tobias Macey and today I'm interviewing Michel Tricot about the journey to the 1.0 launch of Airbyte and what that means for the projectInterview IntroductionHow did you get involved in the area of data management?Can you describe what Airbyte is and the story behind it?What are some of the notable milestones that you have traversed on your path to the 1.0 release?The ecosystem has gone through some significant shifts since you first launched Airbyte. How have trends such as generative AI, the rise and fall of the "modern data stack", and the shifts in investment impacted your overall product and business strategies?What are some of the hard-won lessons that you have learned about the realities of data movement and integration?What are some of the most interesting/challenging/surprising edge cases or performance bottlenecks that you have had to address?What are the core architectural decisions that have proven to be effective?How has the architecture had to change as you progressed to the 1.0 release?A 1.0 version signals a degree of stability and commitment. Can you describe the decision process that you went through in committing to a 1.0 version?What are the most interesting, innovative, or unexpected ways that you have seen Airbyte used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airbyte?When is Airbyte the wrong choice?What do you have planned for the future of Airbyte after the 1.0 launch?Contact Info LinkedInParting 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.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 hosts@dataengineeringpodcast.com with your story.Links AirbytePodcast EpisodeAirbyte CloudAirbyte Connector BuilderSinger ProtocolAirbyte ProtocolAirbyte CDKModern Data StackELTVector DatabasedbtFivetranPodcast EpisodeMeltanoPodcast EpisodedltReverse ETLGraphRAGAI Engineering Podcast EpisodeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

    57 min
  6. SEP 1

    Enhancing Data Accessibility and Governance with Gravitino

    Summary As data architectures become more elaborate and the number of applications of data increases, it becomes increasingly challenging to locate and access the underlying data. Gravitino was created to provide a single interface to locate and query your data. In this episode Junping Du explains how Gravitino works, the capabilities that it unlocks, and how it fits into your data platform. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementYour host is Tobias Macey and today I'm interviewing Junping Du about Gravitino, an open source metadata service for a unified view of all of your schemasInterview IntroductionHow did you get involved in the area of data management?Can you describe what Gravitino is and the story behind it?What problems are you solving with Gravitino?What are the methods that teams have relied on in the absence of Gravitino to address those use cases?What led to the Hive Metastore being the default for so long?What are the opportunities for innovation and new functionality in the metadata service?The documentation suggests that Gravitino has overlap with a number of tool categories such as table schema (Hive metastore), metadata repository (Open Metadata), data federation (Trino/Alluxio). What are the capabilities that it can completely replace, and which will require other systems for more comprehensive functionality?What are the capabilities that you are explicitly keeping out of scope for Gravitino?Can you describe the technical architecture of Gravitino?How have the design and scope evolved from when you first started working on it?Can you describe how Gravitino integrates into an overall data platform?In a typical day, what are the different ways that a data engineer or data analyst might interact with Gravitino?One of the features that you highlight is centralized permissions management. Can you describe the access control model that you use for unifying across underlying sources?What are the most interesting, innovative, or unexpected ways that you have seen Gravitino used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gravitino?When is Gravitino the wrong choice?What do you have planned for the future of Gravitino?Contact Info LinkedInGitHubParting 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.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 hosts@dataengineeringpodcast.com with your story.Links GravitinoHadoopDatastratoPyTorchRayData FabricHiveIcebergPodcast EpisodeHive MetastoreTrinoOpenMetadataPodcast EpisodeAlluxioAtlanPodcast EpisodeSparkThriftThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

    39 min
  7. AUG 4

    The Evolution of DataOps: Insights from DataKitchen's CEO

    Summary In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Chris Berg, CEO of DataKitchen, to discuss his ongoing mission to simplify the lives of data engineers. Chris explains the challenges faced by data engineers, such as constant system failures, the need for rapid changes, and high customer demands. Chris delves into the concept of DataOps, its evolution, and the misappropriation of related terms like data mesh and data observability. He emphasizes the importance of focusing on processes and systems rather than just tools to improve data engineering workflows. Chris also introduces DataKitchen's open-source tools, DataOps TestGen and DataOps Observability, designed to automate data quality validation and monitor data journeys in production. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.Your host is Tobias Macey and today I'm interviewing Chris Bergh about his tireless quest to simplify the lives of data engineersInterview IntroductionHow did you get involved in the area of data management?Can you describe what DataKitchen is and the story behind it?You helped to define and popularize "DataOps", which then went through a journey of misappropriation similar to "DevOps", and has since faded in use. What is your view on the realities of "DataOps" today?Out of the popularized wave of "DataOps" tools came subsequent trends in data observability, data reliability engineering, etc. How have those cycles influenced the way that you think about the work that you are doing at DataKitchen?The data ecosystem went through a massive growth period over the past ~7 years, and we are now entering a cycle of consolidation. What are the fundamental shifts that we have gone through as an industry in the management and application of data?What are the challenges that never went away?You recently open sourced the dataops-testgen and dataops-observability tools. What are the outcomes that you are trying to produce with those projects?What are the areas of overlap with existing tools and what are the unique capabilities that you are offering?Can you talk through the technical implementation of your new obserability and quality testing platform?What does the onboarding and integration process look like?Once a team has one or both tools set up, what are the typical points of interaction that they will have over the course of their workday?What are the most interesting, innovative, or unexpected ways that you have seen dataops-observability/testgen used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on promoting DataOps?What do you have planned for the future of your work at DataKitchen?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Links DataKitchenPodcast EpisodeNASADataOps ManifestoData Reliability EngineeringData ObservabilitydbtDevOps Enterprise SummitBuilding The Data Warehouse by Bill Inmon (affiliate link)dataops-testgen, dataops-observabilityFree Data Quality and Data Observability CertificationDatabricksDORA MetricsDORA for dataThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

    54 min
  8. JUL 28

    Achieving Data Reliability: The Role of Data Contracts in Modern Data Management

    Summary Data contracts are both an enforcement mechanism for data quality, and a promise to downstream consumers. In this episode Tom Baeyens returns to discuss the purpose and scope of data contracts, emphasizing their importance in achieving reliable analytical data and preventing issues before they arise. He explains how data contracts can be used to enforce guarantees and requirements, and how they fit into the broader context of data observability and quality monitoring. The discussion also covers the challenges and benefits of implementing data contracts, the organizational impact, and the potential for standardization in the field. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.At Outshift, the incubation engine from Cisco, they are driving innovation in AI, cloud, and quantum technologies with the powerful combination of enterprise strength and startup agility. Their latest innovation for the AI ecosystem is Motific, addressing a critical gap in going from prototype to production with generative AI. Motific is your vendor and model-agnostic platform for building safe, trustworthy, and cost-effective generative AI solutions in days instead of months. Motific provides easy integration with your organizational data, combined with advanced, customizable policy controls and observability to help ensure compliance throughout the entire process. Move beyond the constraints of traditional AI implementation and ensure your projects are launched quickly and with a firm foundation of trust and efficiency. Go to motific.ai today to learn more!Your host is Tobias Macey and today I'm interviewing Tom Baeyens about using data contracts to build a clearer API for your dataInterview IntroductionHow did you get involved in the area of data management?Can you describe the scope and purpose of data contracts in the context of this conversation?In what way(s) do they differ from data quality/data observability?Data contracts are also known as the API for data, can you elaborate on this?What are the types of guarantees and requirements that you can enforce with these data contracts?What are some examples of constraints or guarantees that cannot be represented in these contracts?Are data contracts related to the shift-left?Data contracts are also known as the API for data, can you elaborate on this?The obvious application of data contracts are in the context of pipeline execution flows to prevent failing checks from propagating further in the data flow. What are some of the other ways that these contracts can be integrated into an organization's data ecosystem?How did you approach the design of the syntax and implementation for Soda's data contracts?Guarantees and constraints around data in different contexts have been implemented in numerous tools and systems. What are the areas of overlap in e.g. dbt, great expectations?Are there any emerging standards or design patterns around data contracts/guarantees that will help encourage portability and integration across tooling/platform contexts?What are the most interesting, innovative, or unexpected ways that you have seen data contracts used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data contracts at Soda?When are data contracts the wrong choice?What do you have planned for the future of data cont

    49 min
4.6
out of 5
131 Ratings

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This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

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