Summary In this episode of the Data Engineering Podcast Viktor Kessler, co-founder of Vakmo, talks about the architectural patterns in the lake house enabled by a fast and feature-rich Iceberg catalog. Viktor shares his journey from data warehouses to developing the open-source project, Lakekeeper, an Apache Iceberg REST catalog written in Rust that facilitates building lake houses with essential components like storage, compute, and catalog management. He discusses the importance of metadata in making data actionable, the evolution of data catalogs, and the challenges and innovations in the space, including integration with OpenFGA for fine-grained access control and managing data across formats and compute engines.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Viktor Kessler about architectural patterns in the lakehouse that are unlocked by a fast and feature-rich Iceberg catalogInterview IntroductionHow did you get involved in the area of data management?Can you describe what LakeKeeper is and the story behind it? What is the core of the problem that you are addressing?There has been a lot of activity in the catalog space recently. What are the driving forces that have highlighted the need for a better metadata catalog in the data lake/distributed data ecosystem?How would you characterize the feature sets/problem spaces that different entrants are focused on addressing?Iceberg as a table format has gained a lot of attention and adoption across the data ecosystem. The REST catalog format has opened the door for numerous implementations. What are the opportunities for innovation and improving user experience in that space?What is the role of the catalog in managing security and governance? (AuthZ, auditing, etc.)What are the channels for propagating identity and permissions to compute engines? (how do you avoid head-scratching about permission denied situations)Can you describe how LakeKeeper is implemented?How have the design and goals of the project changed since you first started working on it?For someone who has an existing set of Iceberg tables and catalog, what does the migration process look like?What new workflows or capabilities does LakeKeeper enable for data teams using Iceberg tables across one or more compute frameworks?What are the most interesting, innovative, or unexpected ways that you have seen LakeKeeper used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on LakeKeeper?When is LakeKeeper the wrong choice?What do you have planned for the future of LakeKeeper?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 [email protected] with your story.Links LakeKeeperSAPMicrosoft AccessMicrosoft ExcelApache IcebergPodcast EpisodeIceberg REST CatalogPyIcebergSparkTrinoDremioHive MetastoreHadoopNATSPolarsDuckDBPodcast EpisodeDataFusionAtlanPodcast EpisodeOpen MetadataPodcast EpisodeApache AtlasOpenFGAHudiPodcast EpisodeDelta LakePodcast EpisodeLance Table FormatPodcast EpisodeUnity CatalogPolaris CatalogApache GravitinoPodcast Episode KeycloakOpen Policy Agent (OPA)Apache RangerApache NiFiThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Jeremy Edberg, CEO of DBOS, about durable execution and its impact on designing and implementing business logic for data systems. Jeremy explains how DBOS's serverless platform and orchestrator provide local resilience and reduce operational overhead, ensuring exactly-once execution in distributed systems through the use of the Transact library. He discusses the importance of version management in long-running workflows and how DBOS simplifies system design by reducing infrastructure needs like queues and CI pipelines, making it beneficial for data pipelines, AI workloads, and agentic AI.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Jeremy Edberg about durable execution and how it influences the design and implementation of business logicInterview IntroductionHow did you get involved in the area of data management?Can you describe what DBOS is and the story behind it?What is durable execution?What are some of the notable ways that inclusion of durable execution in an application architecture changes the ways that the rest of the application is implemented? (e.g. error handling, logic flow, etc.)Many data pipelines involve complex, multi-step workflows. How does DBOS simplify the creation and management of resilient data pipelines? How does durable execution impact the operational complexity of data management systems?One of the complexities in durable execution is managing code/data changes to workflows while existing executions are still processing. What are some of the useful patterns for addressing that challenge and how does DBOS help?Can you describe how DBOS is architected?How have the design and goals of the system changed since you first started working on it?What are the characteristics of Postgres that make it suitable for the persistence mechanism of DBOS?What are the guiding principles that you rely on to determine the boundaries between the open source and commercial elements of DBOS?What are the most interesting, innovative, or unexpected ways that you have seen DBOS used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DBOS?When is DBOS the wrong choice?What do you have planned for the future of DBOS?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 [email protected] with your story.Links DBOSExactly Once SemanticsTemporalSempahorePostgresDBOS TransactPython Typescript Idempotency KeysAgentic AIState MachineYugabyteDBPodcast EpisodeCockroachDBSupabaseNeonPodcast EpisodeAirflowThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Roman Gershman, CTO and founder of Dragonfly DB, explores the development and impact of high-speed in-memory databases. Roman shares his experience creating a more efficient alternative to Redis, focusing on performance gains, scalability, and cost efficiency, while addressing limitations such as high throughput and low latency scenarios. He explains how Dragonfly DB solves operational complexities for users and delves into its technical aspects, including maintaining compatibility with Redis while innovating on memory efficiency. Roman discusses the importance of cost efficiency and operational simplicity in driving adoption and shares insights on the broader ecosystem of in-memory data stores, future directions like SSD tiering and vector search capabilities, and the lessons learned from building a new database engine.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Roman Gershman about building a high-speed in-memory database and the impact of the performance gains on data applicationsInterview IntroductionHow did you get involved in the area of data management?Can you describe what DragonflyDB is and the story behind it?What is the core problem/use case that is solved by making a "faster Redis"?The other major player in the high performance key/value database space is Aerospike. What are the heuristics that an engineer should use to determine whether to use that vs. Dragonfly/Redis?Common use cases for Redis involve application caches and queueing (e.g. Celery/RQ). What are some of the other applications that you have seen Redis/Dragonfly used for, particularly in data engineering use cases?There is a piece of tribal wisdom that it takes 10 years for a database to iron out all of the kinks. At the same time, there have been substantial investments in commoditizing the underlying components of database engines. Can you describe how you approached the implementation of DragonflyDB to arive at a functional and reliable implementation?What are the architectural elements that contribute to the performance and scalability benefits of Dragonfly?How have the design and goals of the system changed since you first started working on it?For teams who migrate from Redis to Dragonfly, beyond the cost savings what are some of the ways that it changes the ways that they think about their overall system design?What are the most interesting, innovative, or unexpected ways that you have seen Dragonfly used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DragonflyDB?When is DragonflyDB the wrong choice?What do you have planned for the future of DragonflyDB?Contact Info GitHubLinkedInParting 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 [email protected] with your story.Links DragonflyDBRedisElasticacheValKeyAerospikeLaravelSidekiqCelerySeastar FrameworkShared-Nothing Architectureio_uringmidi-redisDunning-Kruger EffectRustThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Sean Knapp, CEO of Ascend.io, explores the intersection of AI and data engineering. He discusses the evolution of data engineering and the role of AI in automating processes, alleviating burdens on data engineers, and enabling them to focus on complex tasks and innovation. The conversation covers the challenges and opportunities presented by AI, including the need for intelligent tooling and its potential to streamline data engineering processes. Sean and Tobias also delve into the impact of generative AI on data engineering, highlighting its ability to accelerate development, improve governance, and enhance productivity, while also noting the current limitations and future potential of AI in the field.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Sean Knapp about how Ascend is incorporating AI into their platform to help you keep up with the rapid rate of changeInterview IntroductionHow did you get involved in the area of data management?Can you describe what Ascend is and the story behind it?The last time we spoke was August of 2022. What are the most notable or interesting evolutions in your platform since then?In that same time "AI" has taken up all of the oxygen in the data ecosystem. How has that impacted the ways that you and your customers think about their priorities?The introduction of AI as an API has caused many organizations to try and leap-frog their data maturity journey and jump straight to building with advanced capabilities. How is that impacting the pressures and priorities felt by data teams?At the same time that AI-focused product goals are straining data teams capacities, AI also has the potential to act as an accelerator to their work. What are the roadblocks/speedbumps that are in the way of that capability?Many data teams are incorporating AI tools into parts of their workflow, but it can be clunky and cumbersome. How are you thinking about the fundamental changes in how your platform works with AI at its center?Can you describe the technical architecture that you have evolved toward that allows for AI to drive the experience rather than being a bolt-on?What are the concrete impacts that these new capabilities have on teams who are using Ascend?What are the most interesting, innovative, or unexpected ways that you have seen Ascend + AI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on incorporating AI into the core of Ascend?When is Ascend the wrong choice?What do you have planned for the future of AI in Ascend?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 [email protected] with your story.Links AscendCursor AI Code EditorDevinGitHub CopilotOpenAI DeepResearchS3 TablesAWS GlueAWS BedrockSnowparkCo-Intelligence: Living and Working with AI by Ethan Mollick (affiliate link)OpenAI o3The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Pete DeJoy, co-founder and product lead at Astronomer, talks about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3. Pete shares his journey into data engineering, discusses Astronomer's contributions to the Airflow project, and highlights the critical role of Airflow in powering operational data products. He covers the evolution of Airflow, its position in the data ecosystem, and the challenges faced by data engineers, including infrastructure management and observability. The conversation also touches on the upcoming Airflow 3 release, which introduces data awareness, architectural improvements, and multi-language support, and Astronomer's observability suite, Astro Observe, which provides insights and proactive recommendations for Airflow users.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Pete DeJoy about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3Interview IntroductionCan you describe what Astronomer is and the story behind it?How would you characterize the relationship between Airflow and Astronomer?Astronomer just released your State of Airflow 2025 Report yesterday and it is the largest data engineering survey ever with over 5,000 respondents. Can you talk a bit about top level findings in the report?What about the overall growth of the Airflow project over time?How have the focus and features of Astronomer changed since it was last featured on the show in 2017?Astro Observe GA’d in early February, what does the addition of pipeline observability mean for your customers? What are other capabilities similar in scope to observability that Astronomer is looking at adding to the platform?Why is Airflow so critical in providing an elevated Observability–or cataloging, or something simlar - experience in a DataOps platform? What are the notable evolutions in the Airflow project and ecosystem in that time?What are the core improvements that are planned for Airflow 3.0?What are the most interesting, innovative, or unexpected ways that you have seen Astro used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airflow and Astro?What do you have planned for the future of Astro/Astronomer/Airflow?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 [email protected] with your story.Links AstronomerAirflowMaxime BeaucheminMongoDBDatabricksConfluentSparkKafkaDagsterPodcast EpisodePrefectAirflow 3The Rise of the Data Engineer blog postdbtJupyter NotebookZapiercosmos library for dbt in AirflowRuffAirflow Custom OperatorSnowflakeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large language models (LLMs) to enhance productivity and reduce manual toil. The conversation covers the potential of AI to transform data engineering tasks, such as text-to-SQL interfaces and creating semantic graphs to improve data accessibility, and explores practical applications of LLMs in automating code reviews, testing, and understanding data lineage.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Gleb Mezhanskiy about Interview IntroductionHow did you get involved in the area of data management?modern data stack is deadwhere is AI in the data stack?"buy our tool to ship AI"opportunities for LLM in DE workflowContact 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 [email protected] with your story.Links DatafoldCopilotCursor IDEAI AgentsDataChatAI Engineering Podcast EpisodeMetrics LayerEmacsLangChainLangGraphCrewAIThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Bartosz Mikulski talks about preparing data for AI applications. Bartosz shares his journey from data engineering to MLOps and emphasizes the importance of data testing over software development in AI contexts. He discusses the types of data assets required for AI applications, including extensive test datasets, especially in generative AI, and explains the differences in data requirements for various AI application styles. The conversation also explores the skills data engineers need to transition into AI, such as familiarity with vector databases and new data modeling strategies, and highlights the challenges of evolving AI applications, including frequent reprocessing of data when changing chunking strategies or embedding models.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Bartosz Mikulski about how to prepare data for use in AI applicationsInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining some of the main categories of data assets that are needed for AI applications?How does the nature of the application change those requirements? (e.g. RAG app vs. agent, etc.)How do the different assets map to the stages of the application lifecycle?What are some of the common roles and divisions of responsibility that you see in the construction and operation of a "typical" AI application?For data engineers who are used to data warehousing/BI, what are the skills that map to AI apps?What are some of the data modeling patterns that are needed to support AI apps?chunking strategies metadata managementWhat are the new categories of data that data engineers need to manage in the context of AI applications?agent memory generation/evolution conversation history managementdata collection for fine tuningWhat are some of the notable evolutions in the space of AI applications and their patterns that have happened in the past ~1-2 years that relate to the responsibilities of data engineers?What are some of the skills gaps that teams should be aware of and identify training opportunities for?What are the most interesting, innovative, or unexpected ways that you have seen data teams address the needs of AI applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI applications and their reliance on data?What are some of the emerging trends that you are paying particular attention to?Contact Info WebsiteLinkedInParting 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 [email protected] with your story.Links SparkRayChunking StrategiesHypothetical document embeddingsModel Fine TuningPrompt CompressionThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Andrew Luo, CEO of OneSchema, talks about handling CSV data in business operations. Andrew shares his background in data engineering and CRM migration, which led to the creation of OneSchema, a platform designed to automate CSV imports and improve data validation processes. He discusses the challenges of working with CSVs, including inconsistent type representation, lack of schema information, and technical complexities, and explains how OneSchema addresses these issues using multiple CSV parsers and AI for data type inference and validation. Andrew highlights the business case for OneSchema, emphasizing efficiency gains for companies dealing with large volumes of CSV data, and shares plans to expand support for other data formats and integrate AI-driven transformation packs for specific industries.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Andrew Luo about how OneSchema addresses the headaches of dealing with CSV data for your businessInterview IntroductionHow did you get involved in the area of data management?Despite the years of evolution and improvement in data storage and interchange formats, CSVs are just as prevalent as ever. What are your opinions/theories on why they are so ubiquitous?What are some of the major sources of CSV data for teams that rely on them for business and analytical processes?The most obvious challenge with CSVs is their lack of type information, but they are notorious for having numerous other problems. What are some of the other major challenges involved with using CSVs for data interchange/ingestion?Can you describe what you are building at OneSchema and the story behind it?What are the core problems that you are solving, and for whom?Can you describe how you have architected your platform to be able to manage the variety, volume, and multi-tenancy of data that you process?How have the design and goals of the product changed since you first started working on it?What are some of the major performance issues that you have encountered while dealing with CSV data at scale?What are some of the most surprising things that you have learned about CSVs in the process of building OneSchema?What are the most interesting, innovative, or unexpected ways that you have seen OneSchema used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on OneSchema?When is OneSchema the wrong choice?What do you have planned for the future of OneSchema?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 [email protected] with your story.Links OneSchemaEDI == Electronic Data InterchangeUTF-8 BOM (Byte Order Mark) CharactersSOAPCSV RFCIcebergSSIS == SQL Server Integration ServicesMS AccessDatafusionJSON SchemaSFTP == Secure File Transfer ProtocolThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Dan Bruckner, co-founder and CTO of Tamr, talks about the application of machine learning (ML) and artificial intelligence (AI) in master data management (MDM). Dan shares his journey from working at CERN to becoming a data expert and discusses the challenges of reconciling large-scale organizational data. He explains how data silos arise from independent teams and highlights the importance of combining traditional techniques with modern AI to address the nuances of data reconciliation. Dan emphasizes the transformative potential of large language models (LLMs) in creating more natural user experiences, improving trust in AI-driven data solutions, and simplifying complex data management processes. He also discusses the balance between using AI for complex data problems and the necessity of human oversight to ensure accuracy and trust.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world; like in their episode “The Secret Sauce Behind McDonald’s Data Strategy”, which digs into how AI-driven tools can be used to support crew efficiency and customer interactions. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Dan Bruckner about the application of ML and AI techniques to the challenge of reconciling data at the scale of businessInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of the different ways that organizational data becomes unwieldy and needs to be consolidated and reconciled?How does that reconciliation relate to the practice of "master data management"What are the scaling challenges with the current set of practices for reconciling data?ML has been applied to data cleaning for a long time in the form of entity resolution, etc. How has the landscape evolved or matured in recent years?What (if any) transformative capabilities do LLMs introduce?What are the missing pieces/improvements that are necessary to make current AI systems usable out-of-the-box for data cleaning?What are the strategic decisions that need to be addressed when implementing ML/AI techniques in the data cleaning/reconciliation process?What are the risks involved in bringing ML to bear on data cleaning for inexperienced teams?What are the most interesting, innovative, or unexpected ways that you have seen ML techniques used in data resolution?What are the most interesting, unexpected, or challenging lessons that you have learned while working on using ML/AI in master data management?When is ML/AI the wrong choice for data cleaning/reconciliation?What are your hopes/predictions for the future of ML/AI applications in MDM and data cleaning?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 [email protected] with your story.Links TamrMaster Data ManagementCERNLHCMichael StonebrakerConway's LawExpert SystemsInformation RetrievalActive LearningThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Lior Barak shares his insights on developing a three-year strategic vision for data management. He discusses the importance of having a strategic plan for data, highlighting the need for data teams to focus on impact rather than just enablement. He introduces the concept of a "data vision board" and explains how it can help organizations outline their strategic vision by considering three key forces: regulation, stakeholders, and organizational goals. Lior emphasizes the importance of balancing short-term pressures with long-term strategic goals, quantifying the cost of data issues to prioritize effectively, and maintaining the strategic vision as a living document through regular reviews. He encourages data teams to shift from being enablers to impact creators and provides practical advice on implementing a data vision board, setting clear KPIs, and embracing a product mindset to create tangible business impacts through strategic data management.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementIt’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. Your host is Tobias Macey and today I'm interviewing Lior Barak about how to develop your three year strategic vision for dataInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an outline of the types of problems that occur as a result of not developing a strategic plan for an organization's data systems?What is the format that you recommend for capturing that strategic vision?What are the types of decisions and details that you believe should be included in a vision statement?Why is a 3 year horizon beneficial? What does that scale of time encourage/discourage in the debate and decision-making process?Who are the personas that should be included in the process of developing this strategy document?Can you walk us through the steps and processes involved in developing the data vision board for an organization?What are the time-frames or milestones that should lead to revisiting and revising the strategic objectives?What are the most interesting, innovative, or unexpected ways that you have seen a data vision strategy used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data strategy development?When is a data vision board the wrong choice?What are some additional resources or practices that you recommend teams invest in as a supplement to this strategic vision exercise?Contact Info LinkedInSubstackParting 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 [email protected] with your story.Links Vision Board OverviewEpisode 397: Defining A Strategy For Your Data ProductsMinto Pyramid PrincipleKPI == Key Performance IndicatorOKR == Objectives and Key ResultsPhil Jackson: Eleven Rings (affiliate link)The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary The core task of data engineering is managing the flows of data through an organization. In order to ensure those flows are executing on schedule and without error is the role of the data orchestrator. Which orchestration engine you choose impacts the ways that you architect the rest of your data platform. In this episode Hugo Lu shares his thoughts as the founder of an orchestration company on how to think about data orchestration and data platform design as we navigate the current era of data engineering.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementIt’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world, from big picture questions like AI governance and data sharing to more nuanced questions like, how do we balance offense and defense in data management? In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Hugo Lu about the data platform and orchestration ecosystem and how to navigate the available optionsInterview IntroductionHow did you get involved in building data platforms?Can you describe what an orchestrator is in the context of data platforms?There are many other contexts in which orchestration is necessary. What are some examples of how orchestrators have adapted (or failed to adapt) to the times?What are the core features that are necessary for an orchestrator to have when dealing with data-oriented workflows?Beyond the bare necessities, what are some of the other features and design considerations that go into building a first-class dat platform or orchestration system?There have been several generations of orchestration engines over the past several years. How would you characterize the different coarse groupings of orchestration engines across those generational boundaries?How do the characteristics of a data orchestrator influence the overarching architecture of an organization's data platform/data operations?What about the reverse?How have the cycles of ML and AI workflow requirements impacted the design requirements for data orchestrators?What are the most interesting, innovative, or unexpected ways that you have seen data orchestrators used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration?When is an orchestrator the wrong choice?What are your predictions and/or hopes for the future of data orchestration?Contact Info MediumLinkedInParting Question From your perspective, what is the biggest thing data teams are missing in the technology 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 [email protected] with your story.Links OrchestraPrevious Episode: Overview Of The State Of Data OrchestrationCronArgoCDDAGKubernetesData MeshAirflowSSIS == SQL Server Integration ServicesPentahoKettleDataVoloNiFiPodcast EpisodeDagstergRPCCoalescePodcast EpisodedbtDataHubPalantirThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast the inimitable Max Beauchemin talks about reusability in data pipelines. The conversation explores the "write everything twice" problem, where similar pipelines are built without code reuse, and discusses the challenges of managing different SQL dialects and relational databases. Max also touches on the evolving role of data engineers, drawing parallels with front-end engineering, and suggests that generative AI could facilitate knowledge capture and distribution in data engineering. He encourages the community to share reference implementations and templates to foster collaboration and innovation, and expresses hopes for a future where code reuse becomes more prevalent.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm joined again by Max Beauchemin to talk about the challenges of reusability in data pipelinesInterview IntroductionHow did you get involved in the area of data management?Can you start by sharing your current thesis on the opportunities and shortcomings of code and component reusability in the data context?What are some ways that you think about what constitutes a "component" in this context?The data ecosystem has arguably grown more varied and nuanced in recent years. At the same time, the number and maturity of tools has grown. What is your view on the current trend in productivity for data teams and practitioners?What do you see as the core impediments to building more reusable and general-purpose solutions in data engineering?How can we balance the actual needs of data consumers against their requests (whether well- or un-informed) to help increase our ability to better design our workflows for reuse?In data engineering there are two broad approaches; code-focused or SQL-focused pipelines. In principle one would think that code-focused environments would have better composability. What are you seeing as the realities in your personal experience and what you hear from other teams?When it comes to SQL dialects, dbt offers the option of Jinja macros, whereas SDF and SQLMesh offer automatic translation. There are also tools like PRQL and Malloy that aim to abstract away the underlying SQL. What are the tradeoffs across those options that help or hinder the portability of transformation logic?Which layers of the data stack/steps in the data journey do you see the greatest opportunity for improving the creation of more broadly usable abstractions/reusable elements?low/no code systems for code reuseimpact of LLMs on reusability/compositionimpact of background on industry practices (e.g. DBAs, sysadmins, analysts vs. SWE, etc.)polymorphic data models (e.g. activity schema)What are the most interesting, innovative, or unexpected ways that you have seen teams address composability and reusability of data components?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data-oriented tools and utilities?What are your hopes and predictions for sharing of code and logic in the future of data engineering?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 [email protected] with your story.Links Max's Blog PostAirflowSupersetTableauLookerPowerBICohort AnalysisNextJSAirbytePodcast EpisodeFivetranPodcast EpisodeSegmentdbtSQLMeshPodcast EpisodeSparkLAMP StackPHPRelational AlgebraKnowledge GraphPython MarshmallowData Warehouse Lifecycle Toolkit (affiliate link)Entity Centric Data Modeling Blog PostAmplitudeOSACon presentationol-data-platform Tobias' team's data platform codeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Sam Kleinman talks about the pivotal role of databases in software engineering. Sam shares his journey into the world of data and discusses the complexities of database selection, highlighting the trade-offs between different database architectures and how these choices affect system design, query performance, and the need for ETL processes. He emphasizes the importance of understanding specific requirements to choose the right database engine and warns against over-engineering solutions that can lead to increased complexity. Sam also touches on the tendency of engineers to move logic to the application layer due to skepticism about database longevity and advises teams to leverage database capabilities instead. Finally, he identifies a significant gap in data management tooling: the lack of easy-to-use testing tools for database interactions, highlighting the need for better testing paradigms to ensure reliability and reduce bugs in data-driven applications.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementIt’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. Your host is Tobias Macey and today I'm interviewing Sam Kleinman about database tradeoffs across operating environments and axes of scaleInterview IntroductionHow did you get involved in the area of data management?The database engine you use has a substantial impact on how you architect your overall system. When starting a greenfield project, what do you see as the most important factor to consider when selecting a database?points of friction introduced by database capabilitiesembedded databases (e.g. SQLite, DuckDB, LanceDB), when to use and when do they become a bottlenecksingle-node database engines (e.g. Postgres, MySQL), when are they legitimately a problemdistributed databases (e.g. CockroachDB, PlanetScale, MongoDB)polyglot storage vs. general-purpose/multimodal databasesfederated queries, benefits and limitations ease of integration vs. variability of performance and access control 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 [email protected] with your story.Links MongoDBNeonPodcast EpisodeGlareDBNoSQLS3 Conditional WriteEvent driven architectureCockroachDBCouchbaseCassandraThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary The challenges of integrating all of the tools in the modern data stack has led to a new generation of tools that focus on a fully integrated workflow. At the same time, there have been many approaches to how much of the workflow is driven by code vs. not. Burak Karakan is of the opinion that a fully integrated workflow that is driven entirely by code offers a beneficial and productive means of generating useful analytical outcomes. In this episode he shares how Bruin builds on those opinions and how you can use it to build your own analytics without having to cobble together a suite of tools with conflicting abstractions.

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 Burak Karakan about the benefits of building code-only data systemsInterview IntroductionHow did you get involved in the area of data management?Can you describe what Bruin is and the story behind it?Who is your target audience?There are numerous tools that address the ETL workflow for analytical data. What are the pain points that you are focused on for your target users?How does a code-only approach to data pipelines help in addressing the pain points of analytical workflows?How might it act as a limiting factor for organizational involvement?Can you describe how Bruin is designed?How have the design and scope of Bruin evolved since you first started working on it?You call out the ability to mix SQL and Python for transformation pipelines. What are the components that allow for that functionality?What are some of the ways that the combination of Python and SQL improves ergonomics of transformation workflows?What are the key features of Bruin that help to streamline the efforts of organizations building analytical systems?Can you describe the workflow of someone going from source data to warehouse and dashboard using Bruin and Ingestr?What are the opportunities for contributions to Bruin and Ingestr to expand their capabilities?What are the most interesting, innovative, or unexpected ways that you have seen Bruin and Ingestr used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bruin?When is Bruin the wrong choice?What do you have planned for the future of Bruin?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 [email protected] with your story.Links BruinFivetranStitchIngestrBruin CLIMeltanoSQLGlotdbtSQLMeshPodcast EpisodeSDFPodcast EpisodeAirflowDagsterSnowparkAtlanEvidenceThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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 [email protected] 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

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 [email protected] with your story.Links dltPodcast EpisodePyArrowPolarsIbisDuckDBPodcast Episodedlt Data ContractsRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodePyAirbyteOpenAI o1 ModelLanceDBQDrant EmbeddedAirflowGitHub ActionsArrow DataFusionApache ArrowPyIcebergDelta-RSSCD2 == Slowly Changing DimensionsSQLAlchemySQLGlotFSSpecPydanticSpacyEntity RecognitionParquet File FormatPython DecoratorREST API ToolkitOpenAPI Connector GeneratorConnectorXPython no-GILDelta LakePodcast EpisodeSQLMeshPodcast EpisodeHamiltonTabularPostHogPodcast.init EpisodeAsyncIOCursor.AIData MeshPodcast EpisodeFastAPILangChainGraphRAGAI Engineering Podcast EpisodeProperty GraphPython uvThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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

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 [email protected] 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

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 [email protected] 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

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 contracts?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 [email protected] with your story.Links SodaPodcast EpisodeJBossData ContractAirflowUnit TestingIntegration TestingOpenAPIGraphQLCircuit Breaker PatternSodaCLSoda Data ContractsData MeshGreat Expectationsdbt Unit TestsOpen Data ContractsODCS == Open Data Contract StandardODPS == Open Data Product SpecificationThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary Generative AI has rapidly gained adoption for numerous use cases. To support those applications, organizational data platforms need to add new features and data teams have increased responsibility. In this episode Lior Gavish, co-founder of Monte Carlo, discusses the various ways that data teams are evolving to support AI powered features and how they are incorporating AI into their work. 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 Lior Gavish about the impact of AI on data engineersInterview IntroductionHow did you get involved in the area of data management?Can you start by clarifying what we are discussing when we say "AI"?Previous generations of machine learning (e.g. deep learning, reinforcement learning, etc.) required new features in the data platform. What new demands is the current generation of AI introducing?Generative AI also has the potential to be incorporated in the creation/execution of data pipelines. What are the risk/reward tradeoffs that you have seen in practice?What are the areas where LLMs have proven useful/effective in data engineering?Vector embeddings have rapidly become a ubiquitous data format as a result of the growth in retrieval augmented generation (RAG) for AI applications. What are the end-to-end operational requirements to support this use case effectively?As with all data, the reliability and quality of the vectors will impact the viability of the AI application. What are the different failure modes/quality metrics/error conditions that they are subject to?As much as vectors, vector databases, RAG, etc. seem exotic and new, it is all ultimately shades of the same work that we have been doing for years. What are the areas of overlap in the work required for running the current generation of AI, and what are the areas where it diverges?What new skills do data teams need to acquire to be effective in supporting AI applications?What are the most interesting, innovative, or unexpected ways that you have seen AI impact data engineering teams?What are the most interesting, unexpected, or challenging lessons that you have learned while working with the current generation of AI?When is AI the wrong choice?What are your predictions for the future impact of AI on data engineering teams?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 [email protected] with your Links Monte CarloPodcast EpisodeNLP == Natural Language ProcessingLarge Language ModelsGenerative AIMLOpsML EngineerFeature StoreRetrieval Augmented Generation (RAG)LangchainThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode Praveen Gujar, Director of Product at LinkedIn, talks about the intricacies of product management for data and analytical platforms. Praveen shares his journey from Amazon to Twitter and now LinkedIn, highlighting his extensive experience in building data products and platforms, digital advertising, AI, and cloud services. He discusses the evolving role of product managers in data-centric environments, emphasizing the importance of clean, reliable, and compliant data. Praveen also delves into the challenges of building scalable data platforms, the need for organizational and cultural alignment, and the critical role of product managers in bridging the gap between engineering and business teams. He provides insights into the complexities of platformization, the significance of long-term planning, and the necessity of having a strong relationship with engineering teams. The episode concludes with Praveen offering advice for aspiring product managers and discussing the future of data management in the context of AI and regulatory compliance.

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 Praveen Gujar about product management for data and analytical platformsInterview IntroductionHow did you get involved in the area of data management?Product management is typically thought of as being oriented toward customer facing functionality and features. What is involved in being a product manager for data systems?Many data-oriented products that are customer facing require substantial technical capacity to serve those use cases. How does that influence the process of determining what features to provide/create?investment in technical capacity/platformsidentifying groupings of features that can be served by a common platform investmentmanaging organizational pressures between engineering, product, business, finance, etc.What are the most interesting, innovative, or unexpected ways that you have seen "Data Products & Platforms @ Big-tech" used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on "Building Data Products & Platforms for Big-tech"?When is "Data Products & Platforms @ Big-tech" the wrong choice?What do you have planned for the future of "Data Products & Platforms @ Big-tech"?Contact Info LinkedInWebsiteParting 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 [email protected] with your story.Links DataHubPodcast EpisodeRAG == Retrieval Augmented GenerationThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary Postgres is one of the most widely respected and liked database engines ever. To make it even easier to use for developers to use, Nikita Shamgunov decided to makee it serverless, so that it can scale from zero to infinity. In this episode he explains the engineering involved to make that possible, as well as the numerous details that he and his team are packing into the Neon service to make it even more attractive for anyone who wants to build on top of Postgres. 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 Nikita Shamgunov about his work on making Postgres a serverless database at Neon.Interview IntroductionHow did you get involved in the area of data management?Can you describe what Neon is and the story behind it?The ecosystem around Postgres is large and varied. What are the pain points that you are trying to address with Neon? What does it mean for a database to be serverless?What kinds of products and services are unlocked by making Postgres a serverless database?How does your vision for Neon compare/contrast with what you know of PlanetScale?Postgres is known for having a large ecosystem of plugins that add a lot of interesting and useful features, but the storage layer has not been as easily extensible historically. How have architectural changes in recent Postgres releases enabled your work on Neon?What are the core pieces of engineering that you have had to complete to make Neon possible?How have the design and goals of the project evolved since you first started working on it?The separation of storage and compute is one of the most fundamental promises of the cloud. What new capabilities does that enable in Postgres?How does the branching functionality change the ways that development teams are able to deliver and debug features?Because the storage is now a networked system, what new performance/latency challenges does that introduce? How have you addressed them in Neon?Anyone who has ever operated a Postgres instance has had to tackle the upgrade process. How does Neon address that process for end users?The rampant growth of AI has touched almost every aspect of computing, and Postgres is no exception. How does the introduction of pgvector and semantic/similarity search functionality impact the adoption and usage patterns of Postgres/Neon?What new challenges does that introduce for you as an operator and business owner?What are the lessons that you learned from MemSQL/SingleStore that have been most helpful in your work at Neon?What are the most interesting, innovative, or unexpected ways that you have seen Neon used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Neon?When is Neon the wrong choice? Postgres?What do you have planned for the future of Neon?Contact Info @nikitabase on TwitterLinkedInParting 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 Machine Learning Podcast helps you go from idea to production with machine learning.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.Links NeonPostgreSQLNeon GithubPHPMySQLSQL ServerSingleStorePodcast EpisodeAWS AuroraKhosla VenturesYugabyteDBPodcast EpisodeCockroachDBPodcast EpisodePlanetScalePodcast EpisodeClickhousePodcast EpisodeDuckDBPodcast EpisodeWAL == Write-Ahead LogPgBouncerPureStoragePaxos)HNSW IndexIVF Flat IndexRAG == Retrieval Augmented GenerationAlloyDBNeon Serverless DriverDevinmagic.devThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary This episode features an insightful conversation with Petr Janda, the CEO and founder of Synq. Petr shares his journey from being an engineer to founding Synq, emphasizing the importance of treating data systems with the same rigor as engineering systems. He discusses the challenges and solutions in data reliability, including the need for transparency and ownership in data systems. Synq's platform helps data teams manage incidents, understand data dependencies, and ensure data quality by providing insights and automation capabilities. Petr emphasizes the need for a holistic approach to data reliability, integrating data systems into broader business processes. He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this. 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 Petr Janda about Synq, a data reliability platform focused on leveling up data teams by supporting a culture of engineering rigorInterview IntroductionHow did you get involved in the area of data management?Can you describe what Synq is and the story behind it? Data observability/reliability is a category that grew rapidly over the past ~5 years and has several vendors focused on different elements of the problem. What are the capabilities that you saw as lacking in the ecosystem which you are looking to address?Operational/infrastructure engineers have spent the past decade honing their approach to incident management and uptime commitments. How do those concepts map to the responsibilities and workflows of data teams? Tooling only plays a small part in SLAs and incident management. How does Synq help to support the cultural transformation that is necessary?What does an on-call rotation for a data engineer/data platform engineer look like as compared with an application-focused team?How does the focus on data assets/data products shift your approach to observability as compared to a table/pipeline centric approach?With the focus on sharing ownership beyond the boundaries on the data team there is a strong correlation with data governance principles. How do you see organizations incorporating Synq into their approach to data governance/compliance?Can you describe how Synq is designed/implemented? How have the scope and goals of the product changed since you first started working on it?For a team who is onboarding onto Synq, what are the steps required to get it integrated into their technology stack and workflows?What are the types of incidents/errors that you are able to identify and alert on? What does a typical incident/error resolution process look like with Synq?What are the most interesting, innovative, or unexpected ways that you have seen Synq used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Synq?When is Synq the wrong choice?What do you have planned for the future of Synq?Contact Info LinkedInSubstackParting 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 Machine Learning Podcast helps you go from idea to production with machine learning.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.Links SynqIncident ManagementSLA == Service Level AgreementData GovernancePodcast EpisodePagerDutyOpsGenieClickhousePodcast EpisodedbtPodcast EpisodeSQLMeshPodcast EpisodeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary

Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data 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 Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou

Interview

Introduction How did you get involved in the area of data management? Can you describe what Microsoft Fabric is and the story behind it? Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend? Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution?

What are the elements of Fabric that were engineered specifically for the service? What are the most interesting/complicated integration challenges?

How has your prior experience with Ahana and Presto informed your current work at Microsoft? AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine?

What are the challenges in terms of safety and reliability?

What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically? When is Fabric the wrong choice? What do you have planned for the future of data lake analytics?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

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Links

Microsoft Fabric Ahana episode DB2 Distributed Spark Presto Azure Data MAD Landscape

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Tableau dbt Medallion Architecture Microsoft Onelake ORC Parquet Avro Delta Lake Iceberg

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Hudi

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Hadoop PowerBI

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Velox Gluten Apache XTable GraphQL Formula 1 McLaren

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Starburst:

This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by T