At PyData Berlin, community members and industry voices highlighted how AI and data tooling are evolving across knowledge graphs, MLOps, small-model fine-tuning, explainability, and developer advocacy.

• Igor Kvachenok (Leuphana University / ProKube) combined knowledge graphs with LLMs for structured data extraction in the polymer industry, and noted how MLOps is shifting toward LLM-focused workflows.
• Selim Nowicki (Distill Labs) introduced a platform that uses knowledge distillation to fine-tune smaller models efficiently, making model specialization faster and more accessible.
• Gülsah Durmaz (Architect & Developer) shared her transition from architecture to coding, creating Python tools for design automation and volunteering with PyData through PyLadies.
• Yashasvi Misra (Pure Storage) spoke on explainable AI, stressing accountability and compliance, and shared her perspective as both a data engineer and active Python community organizer.
• Mehdi Ouazza (MotherDuck) reflected on developer advocacy through video, workshops, and branding, showing how creative communication boosts adoption of open-source tools like DuckDB.

Igor Kvachenok Master’s student in Data Science at Leuphana University of Lüneburg, writing a thesis on LLM-enhanced data extraction for the polymer industry. Builds RDF knowledge graphs from semi-structured documents and works at ProKube on MLOps platforms powered by Kubeflow and Kubernetes.

Connect: https://www.linkedin.com/in/igor-kvachenok/

Selim Nowicki Founder of Distill Labs, a startup making small-model fine-tuning simple and fast with knowledge distillation. Previously led data teams at Berlin startups like Delivery Hero, Trade Republic, and Tier Mobility. Sees parallels between today’s ML tooling and dbt’s impact on analytics.

Connect: https://www.linkedin.com/in/selim-nowicki/

Gülsah Durmaz Architect turned developer, creating Python-based tools for architectural design automation with Rhino and Grasshopper. Active in PyLadies and a volunteer at PyData Berlin, she values the community for networking and learning, and aims to bring ML into architecture workflows.

Connect: https://www.linkedin.com/in/gulsah-durmaz/

Yashasvi (Yashi) Misra Data Engineer at Pure Storage, community organizer with PyLadies India, PyCon India, and Women Techmakers. Advocates for inclusive spaces in tech and speaks on explainable AI, bridging her day-to-day in data engineering with her passion for ethical ML.

Connect: https://www.linkedin.com/in/misrayashasvi/

Mehdi Ouazza Developer Advocate at MotherDuck, formerly a data engineer, now focused on building community and education around DuckDB. Runs popular YouTube channels ("mehdio DataTV" and "MotherDuck") and delivered a hands-on workshop at PyData Berlin. Blends technical clarity with creative storytelling.

Connect: https://www.linkedin.com/in/mehd-io/

Summary In this episode of the Data Engineering Podcast Hannes Mühleisen and Mark Raasveldt, the creators of DuckDB, share their work on Duck Lake, a new entrant in the open lakehouse ecosystem. They discuss how Duck Lake, is focused on simplicity, flexibility, and offers a unified catalog and table format compared to other lakehouse formats like Iceberg and Delta. Hannes and Mark share insights into how Duck Lake revolutionizes data architecture by enabling local-first data processing, simplifying deployment of lakehouse solutions, and offering benefits such as encryption features, data inlining, and integration with existing ecosystems.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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 Hannes Mühleisen and Mark Raasveldt about DuckLake, the latest entrant into the open lakehouse ecosystemInterview IntroductionHow did you get involved in the area of data management?Can you describe what DuckLake is and the story behind it?What are the particular problems that DuckLake is solving for?How does this compare to the capabilities of MotherDuck?Iceberg and Delta already have a well established ecosystem, but so does DuckDB. Who are the primary personas that you are trying to focus on in these early days of DuckLake?One of the major factors driving the adoption of formats like Iceberg is cost efficiency for large volumes of data. That brings with it challenges of large batch processing of data. How does DuckLake account for these axes of scale?There is also a substantial investment in the ecosystem of technologies that support Iceberg. The most notable ecosystem challenge for DuckDB and DuckLake is in the query layer. How are you thinking about the evolution and growth of that capability beyond DuckDB (e.g. support in Trino/Spark/Flink)?What are your opinions on the viability of a future where DuckLake and Iceberg become a unified standard and implementation? (why can't Iceberg REST catalog implementations just use DuckLake under the hood?)Digging into the specifics of the specification and implementation, what are some of the capabilities that it offers above and beyond Iceberg?Is it now possible to enforce PK/FK constraints, indexing on underlying data?Given that DuckDB has a vector type, how do you think about the support for vector storage/indexing?How do the capabilities of DuckLake and the integration with DuckDB change the ways that data teams design their data architecture and access patterns?What are your thoughts on the impact of "data gravity" in today's data ecosystem, with engines like DuckDB, KuzuDB, LanceDB, etc. available for embedded and edge use cases?What are the most interesting, innovative, or unexpected ways that you have seen DuckLake used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DuckLake?When is DuckLake the wrong choice?What do you have planned for the future of DuckLake?Contact Info HannesWebsiteMarkWebsiteParting 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 DuckDBPodcast EpisodeDuckLakeDuckDB LabsMySQLCWIMonetDBIcebergIceberg REST CatalogDeltaHudiLanceDuckDB Iceberg ConnectorACID == Atomicity, Consistency, Isolation, DurabilityMotherDuckMotherDuck Managed DuckLakeTrinoSparkPrestoSpark DuckLake DemoDelta KernelArrowdltS3 TablesAttribute Based Access Control (ABAC)ParquetArrow FlightHadoopHDFSDuckLake RoadmapThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

In this episode, we talk with Orell about his journey from electrical engineering to freelancing in data engineering. Exploring lessons from startup life, working with messy industrial data, the realities of freelancing, and how to stay up to date with new tools.

Topics covered: Why Orel left a PhD and a simulation‑focused start‑up after Covid hitWhat he learned trying (and failing) to commercialise medical‑imaging simulationsThe first freelance project and the long, quiet months that followedHow he now finds clients, keeps projects small and delivers value quicklyTypical work he does for industrial companies: parsing messy machine logs, building simple pipelines, adding structure laterFavorite everyday tools (Python, DuckDB, a bit of C++) and the habit of blocking time for learningAdvice for anyone thinking about freelancing: cash runway, networking, and focusing on problems rather than “perfect” tech choices A practical conversation for listeners who are curious about moving from research or permanent roles into freelance data engineering.

🕒 TIMECODES 0:00 Orel’s career and move to freelancing 9:04 Startup experience and data engineering lessons 16:05 Academia vs. startups and starting freelancing 25:33 Early freelancing challenges and networking 34:22 Freelance data engineering and messy industrial data 43:27 Staying practical, learning tools, and growth 50:33 Freelancing challenges and client acquisition 58:37 Tools, problem-solving, and manual work

🔗 CONNECT WITH ORELL Twitter - https://bsky.app/profile/orgarten.bsk... LinkedIn - / ogarten
Github - https://github.com/orgarten Website - https://orellgarten.com

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events GitHub: https://github.com/DataTalksClub LinkedIn - / datatalks-club
Twitter - / datatalksclub
Website - https://datatalks.club/

🔗 CONNECT WITH ALEXEY Connect with Alexey Twitter - / al_grigor
Linkedin - / agrigorev

This is a free preview of a paid episode. To hear more, visit dataengineeringcentral.substack.com

Hello! A new episode of the Data Engineering Central Podcast is dropping today, we will be covering a few hot topics! * Apache Iceberg Catalogs * new Boring Catalog * new full Iceberg support from Databricks/Unity Catalog * Databricks SQL Scripting * DuckDB coming to a Lake House near you * Lakebase from Databricks Going to be a great show, come along for the ride! Thanks …

Hannes Mühleisen shows off DuckLake and answers live questions.DuckLake: https://duckdb.org/2025/05/27/ducklake.html

Imagine writing SQL and getting instant results as you type? Yes, this is reality now. It's amazing!DuckDB/MotherDuck's Instant SQL made a big splash at last month's Data Council. Hamilton Ulmer gives a demo of Instant SQL at the Practical Data Community.----------------------------Instant SQL: https://motherduck.com/blog/introducing-instant-sql/Practical Data Community Discord: https://discord.gg/gNfw5AKWSK

In this podcast episode, we talked with Adrian Brudaru about ​the past, present and future of data engineering.

About the speaker: Adrian Brudaru studied economics in Romania but soon got bored with how creative the industry was, and chose to go instead for the more factual side. He ended up in Berlin at the age of 25 and started a role as a business analyst. At the age of 30, he had enough of startups and decided to join a corporation, but quickly found out that it did not provide the challenge he wanted. As going back to startups was not a desirable option either, he decided to postpone his decision by taking freelance work and has never looked back since. Five years later, he co-founded a company in the data space to try new things. This company is also looking to release open source tools to help democratize data engineering.

0:00 Introduction to DataTalks.Club 1:05 Discussing trends in data engineering with Adrian 2:03 Adrian's background and journey into data engineering 5:04 Growth and updates on Adrian's company, DLT Hub 9:05 Challenges and specialization in data engineering today 13:00 Opportunities for data engineers entering the field 15:00 The "Modern Data Stack" and its evolution 17:25 Emerging trends: AI integration and Iceberg technology 27:40 DuckDB and the emergence of portable, cost-effective data stacks 32:14 The rise and impact of dbt in data engineering 34:08 Alternatives to dbt: SQLMesh and others 35:25 Workflow orchestration tools: Airflow, Dagster, Prefect, and GitHub Actions 37:20 Audience questions: Career focus in data roles and AI engineering overlaps 39:00 The role of semantics in data and AI workflows 41:11 Focusing on learning concepts over tools when entering the field 45:15 Transitioning from backend to data engineering: challenges and opportunities 47:48 Current state of the data engineering job market in Europe and beyond 49:05 Introduction to Apache Iceberg, Delta, and Hudi file formats 50:40 Suitability of these formats for batch and streaming workloads 52:29 Tools for streaming: Kafka, SQS, and related trends 58:07 Building AI agents and enabling intelligent data applications 59:09Closing discussion on the place of tools like DBT in the ecosystem

🔗 CONNECT WITH ADRIAN BRUDARU Linkedin -  / data-team   Website - https://adrian.brudaru.com/ 🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events LinkedIn -  /datatalks-club   Twitter -  /datatalksclub   Website - https://datatalks.club/

It’s time for another episode of the Data Engineering Central Podcast. In this episode, we cover … * AWS Lambda + DuckDB and Delta Lake (Polars, Daft, etc). * IAC - Long Live Terraform. * Databricks Data Quality with DQX. * Unity Catalog releases for DuckDB and Polars * Bespoke vs Managed Data Platforms * Delta Lake vs. Iceberg and UinFORM for a single table. Thanks for b…

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

Hannes Muhleisen is the creator of DuckDB and CEO of DuckDB Labs. We finally got a chance to meet in person at the Forward Data Conference in Paris. We hit it off immediately, and at times, I felt like I was talking with my long lost brother. Hannes is a very cool guy!

While at the conference, we recorded a chat about all things DuckDB, the challenges of data lakehouses and open table formats, local-first tech, and much more. 🦆 🐥

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

It’s time for another episode of the Data Engineering Central Podcast. In this episode we cover … * Apache Airflow vs Databricks Workflows * End-of-Year Engineering Planning for 2025 * 10 Billion Row Challenge with DuckDB vs Daft vs Polars * Raw Data Ingestion. As usual, the full episode is available to paid subscribers, and a shortened version to you free loaders out there, don’t worry, I still love you though.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

It’s time for another episode of Data Engineering Central Podcast, our third one! Topics in this episode … * Should you use DuckDB or Polars? * Small Engineering Changes (PR Reviews) * Daft vs Spark on Databricks with Unity Catalog (Delta Lake) * Primary and Foreign keys in the Lake House Enjoy!

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

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

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. We dive into conversations smoother than your morning coffee (but let’s be honest, just as caffeinated) where industry insights meet light-hearted banter. Whether you’re a data wizard or just curious about the digital chaos around us, kick back and get ready to talk shop—unplugged style! In this episode: Farewell Pandas, Hello Future: Pandas is out, and Ibis is in. We're talking faster, smarter data processing—featuring the rise of DuckDB and the powerhouse that is Polars. Is this the end of an era for Pandas?UV vs. Rye: Forget pip—are these new Python package managers built in Rust the future? We break down UV, Rye, and what it all means for your next Python project.AI-Generated Podcasts: Is AI about to take over your favorite podcasts? We explore the potential of Google’s Notebook LM to transform content into audio gold.When AI Steals Your Voice: Jeff Geerling’s voice gets cloned by AI—without his consent. We dive into the wild world of voice cloning, the ethics, and the future of AI-generated media.Hacking AI with Prompt Injection: Could you outsmart AI? We share some wild strategies from the game Gandalf that challenge your prompt injection skills and teach you how to jailbreak even the toughest guardrails.Jony Ive’s New Gadget Rumor: Is Jony Ive plotting an Apple killer? Rumors are swirling about a new AI-powered handheld device that could shake up the smartphone market.Zero-Downtime Deployments with Kamal Proxy: No more downtime! We geek out over Kamal Proxy, the sleek HTTP tool designed for effortless Docker deployments.Function Calling and LLMs: Get ready for the next evolution in AI—function calling. We discuss its rise in LLMs and dive into the Gorilla project, the leaderboard testing the future of smart APIs.

Jordan Tigani is back to chat about why small data is awesome, data lakehouses, DuckDB, AI, and much more.

Motherduck: https://motherduck.com/

LinkedIn: https://www.linkedin.com/in/jordantigani/

Twitter: https://twitter.com/jrdntgn?lang=en

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! Bookmarklet Maker: Discover how to automate tasks with the Bookmarklet Maker, a tool for turning scripts into handy browser bookmarks. RouteLLM Framework: Explore the RouteLLM framework by LMSys and Anyscale, designed to optimize the cost-performance ratio of LLM routers. Learn more about this collaboration at LMSys and Anyscale. Q for SQL on CSV/TSV: Meet Q, a command-line tool that lets you run SQL queries directly on CSV or TSV files, simplifying data exploration from your terminal. DuckDB Community Extensions: Check out the latest updates in DuckDB's community extensions and see how this database system is evolving. Apple Intelligence and AI Maximalism: Explore Apple's AI strategy, their avoidance of chat UIs, risk management with OpenAI, and the shift of compute costs to users. Being Glue: Delve into the challenges of being "Glue" at work. Explore why women are more likely to take on non-promotable work and how this affects career progression and workplace dynamics.

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style!

In this episode, we're thrilled to have special guest Mehdi Ouazza  diving into a plethora of hot tech topics: Mehdi Ouazza's Insights into his career, online community and working with DuckDB and MotherDuck.Demystifying DevRel: Definitions and distinctions in the realm of tech influence (dive deeper here).Terraform's Licensing Shift: Reactions to HashiCorp's recent changes and its new IBM collaboration, more details here.Github Copilot Workspace: Exploring the latest in AI-powered coding assistance, comparing with devin.ai and CodySnowflake's Arctic LLM: Discussing the latest enterprise AI capabilities and their real-world applications. Read more about Arctic - what it excels at, and how its performance was measuredMore legal kerfuffle in the GenAI realm: The ongoing legal debates around AI's use in creative industries, highlighted by a dispute over Drake’s use of late rapper Tupac’s AI-generated voice in diss track & the licensing deal between Financial Times and OpenAIFuture of Data Engineering: Examining the integration of LLMs into data engineering tools. Insights on prompt-based feature engineering and Databricks' English SDKAI in Music Creation: A little bonus with an AI generated song about Murilo, created with Suno

Ryan Boyd and I chat about the evolution and future of databases, the pendulum between single-server and distributed computing, DuckDB and Motherduck, and much more.

We also talk about developer relations, which I consider Ryan as one of the OG's in the field.

Note - this was recorded the week of Databricks Summit 2023.

If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Summary

The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process

Interview

Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it?

At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape?

As the data landscape matures, how have you seen that influence the types of projects/companies that are founded?

What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive?

The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve?

Is there a different architectural paradigm that you see as growing to take its place?

How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape?

Contact Info

Website @mattturck on Twitter MAD Landscape Comments Email

Parting Question

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

Closing Announcements

Thank you for listening! Don't forget to check out our other 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. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

MAD Landscape First Mark Capital Bayesian Learning AI Winter Databricks Cloud Native Landscape LUMA Scape Hadoop Ecosystem Modern Data Stack Reverse ETL Generative AI dbt Transform

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Summary

Business intelligence has gone through many generational shifts, but each generation has largely maintained the same workflow. Data analysts create reports that are used by the business to understand and direct the business, but the process is very labor and time intensive. The team at Omni have taken a new approach by automatically building models based on the queries that are executed. In this episode Chris Merrick shares how they manage integration and automation around the modeling layer and how it improves the organizational experience of business intelligence.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions! Your host is Tobias Macey and today I'm interviewing Chris Merrick about the Omni Analytics platform and how they are adding automatic data modeling to your business intelligence

Interview

Introduction How did you get involved in the area of data management? Can you describe what Omni Analytics is and the story behind it?

What are the core goals that you are trying to achieve with building Omni?

Business intelligence has gone through many evolutions. What are the unique capabilities that Omni Analytics offers over other players in the market?

What are the technical and organizational anti-patterns that typically grow up around BI systems?

What are the elements that contribute to BI being such a difficult product to use effectively in an organization?

Can you describe how you have implemented the Omni platform?

How have the design/scope/goals of the product changed since you first started working on it?

What does the workflow for a team using Omni look like?

What are some of the developments in the broader ecosystem that have made your work possible?

What are some of the positive and negative inspirations that you have drawn from the experience that you and your team-mates have gained in previous businesses?

What are the most interesting, innovative, or unexpected ways that you have seen Omni used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Omni?

When is Omni the wrong choice?

What do you have planned for the future of Omni?

Contact Info

LinkedIn @cmerrick on Twitter

Parting Question

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

Closing Announcements

Thank you for listening! Don't forget to check out our other 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. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

Omni Analytics Stitch RJ Metrics Looker

Podcast Episode

Singer dbt

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Teradata Fivetran Apache Arrow

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DuckDB

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BigQuery Snowflake

Podcast Episode

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

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