Data Engineering Podcast

FEB 1, 2026

Branches, Diffs, and SQL: How Dolt Powers Agentic Workflows

56 MIN

JAN 25, 2026

Logical First, Physical Second: A Pragmatic Path to Trusted Data

Summary  In this episode of the Data Engineering Podcast Jamie Knowles, Product Director for ER/Studio, talks about data architecture and its importance in driving business meaning. He discusses how data architecture should start with business meaning, not just physical schemas, and explores the pitfalls of jumping straight to physical designs. Jamie shares his practical definition of data architecture centered on shared semantic models that anchor transactional, analytical, and event-driven systems. The conversation covers strategies for evolving an architecture in tandem with delivery, including defining core concepts, aligning teams through governance, and treating the model as a living product. He also examines how generative AI can both help and harm data architecture, accelerating first drafts but amplifying risk without a human-approved ontology. Jamie emphasizes the importance of doing the hard work upfront to make meaning explicit, keeping models simple and business-aligned, and using tools and patterns to reuse that meaning everywhere.  Announcements  <ul><li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li><li>If you lead a data team, you know this pain: Every department needs dashboards, reports, custom views, and they all come to you. So you're either the bottleneck slowing everyone down, or you're spending all your time building one-off tools instead of doing actual data work. Retool gives you a way to break that cycle. Their platform lets people build custom apps on your company data—while keeping it all secure. Type a prompt like 'Build me a self-service reporting tool that lets teams query customer metrics from Databricks—and they get a production-ready app with the permissions and governance built in. They can self-serve, and you get your time back. It's data democratization without the chaos. Check out Retool at <a href="https://www.dataengineeringpodcast.com/retool" target="_blank">dataengineeringpodcast.com/retool</a> today and see how other data teams are scaling self-service. Because let's be honest—we all need to Retool how we handle data requests.</li><li>You’re a developer who wants to innovate—instead, you’re stuck fixing bottlenecks and fighting legacy code. MongoDB can help. It’s a flexible, unified platform that’s built for developers, by developers. MongoDB is ACID compliant, Enterprise-ready, with the capabilities you need to ship AI apps—fast. That’s why so many of the Fortune 500 trust MongoDB with their most critical workloads. Ready to think outside rows and columns? Start building at <a href="https://mongodb.com/Build" target="_blank">MongoDB.com/Build</a></li><li>Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to <a href="https://www.dataengineeringpodcast.com/bruin" target="_blank">dataengineeringpodcast.com/bruin</a> today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.</li><li>Your host is Tobias Macey and today I'm interviewing Jamie Knowles about the impact that a well-developed data architecture (or lack thereof) has on data engineering work</li></ul> Interview <ul><li>Introduction</li><li>How did you get involved in the area of data management?</li><li>Can you start by giving your definition of "data architecture" and what it encompasses?</li><li>How does the nuance change depending on the type of system you are designing? (e.g. data warehouse vs. transactional application database vs. event-driven streaming service)</li><li>In application teams that are large enough there is typically a software architect, but that work often ends up happening organically through trial and error. Who is the responsible party for designing and enforcing a proper data architecture?</li><li>There have been several generational shifts in approach to data warehouse projects in particular. What are some of the anti-patterns that crop up when there is no-one forming a strong opinion on the design/architecture of the warehouse?</li><li>The current stage is largely defined by the ELT pattern. What are some of the ways that workflow can encourage shortcuts?</li><li>Often the need for a proper architecture isn't felt until an organic architecture has developed. What are some of the ways that teams can short-circuit that pain and iterate toward a more sustainable design?</li><li>The common theme in all of the data architecture conversations that I've had is the need for business involvement. There is also a strong push for the business to just want the engineers to deliver data. What are some of the ways that AI utilities can help to accelerate delivery while also capturing business context?</li><li>For teams that are already neck deep in a messy architecture, what are the strategies and tactics that they need to start working toward today to get to a better data architecture?</li><li>What are the most interesting, innovative, or unexpected ways that you have seen teams approach the creation and implementation of their data architecture?</li><li>What are the most interesting, unexpected, or challenging lessons that you have learned while working in data architecture?</li><li>How do you see the introduction of AI at each stage of the data lifecycle changing the ways that teams think about their architectural needs?</li></ul> Contact Info <ul><li><a href="https://www.linkedin.com/in/jamieknowlesltd?originalSubdomain=uk" target="_blank">LinkedIn</a></li></ul> Parting Question <ul><li>From your perspective, what is the biggest gap in the tooling or technology for data management today?</li></ul> Closing Announcements <ul><li>Thank you for listening! Don't forget to check out our other shows. <a href="https://www.pythonpodcast.com" target="_blank">Podcast.__init__</a> covers the Python language, its community, and the innovative ways it is being used. The <a href="https://www.aiengineeringpodcast.com" target="_blank">AI Engineering Podcast</a> is your guide to the fast-moving world of building AI systems.</li><li>Visit the <a href="https://www.dataengineeringpodcast.com" target="_blank">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li><li>If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.</li></ul> Links <ul><li><a href="https://www.idera.com/" target="_blank">Idera</a></li><li><a href="https://erstudio.com/" target="_blank">ER Studio</a></li><li><a href="https://en.wikipedia.org/wiki/Extract,_load,_transform" target="_blank">ELT</a></li><li><a href="https://en.wikipedia.org/wiki/Resource_Description_Framework" target="_blank">RDF == Resource Description Framework</a></li><li><a href="https://en.wikipedia.org/wiki/Object%E2%80%93relational_mapping" target="_blank">ORM == Object-Relational Mapping</a></li></ul> The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA</a>

40 MIN

JAN 18, 2026

Your Data, Your Lake: How Observe Uses Iceberg and Streaming ETL for Observability

72 MIN

JAN 12, 2026

Semantic Operators Meet Dataframes: Building Context for Agents with FENIC

56 MIN

JAN 5, 2026

Beyond Dashboards: How Data Teams Earn a Seat at the Table

Summary  In this episode Goutham Budati about his Data–Perspective–Action framework and how it empowers data teams to become true business partners. Gautham traces his path from automating Excel reports to leading high‑impact data organizations, then breaks down why technical excellence alone isn’t enough: teams must pair reliable data systems with deliberate storytelling, clear problem framing, and concrete action plans. He digs into tactics for moving from reactive ticket-taking to proactive influence — weekly one‑page narratives, design-first discovery, sampling stakeholders for real pain points, and treating dashboards as living roadmaps. He also explores how to right-size technical scope, preserve trust in core metrics, organize teams as “build” and “storytelling” duos, and translate business macros and micros into resilient system designs.  Announcements  <ul><li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li><li>Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to <a href="https://www.dataengineeringpodcast.com/bruin" target="_blank">dataengineeringpodcast.com/bruin</a> today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.</li><li>You’re a developer who wants to innovate—instead, you’re stuck fixing bottlenecks and fighting legacy code. MongoDB can help. It’s a flexible, unified platform that’s built for developers, by developers. MongoDB is ACID compliant, Enterprise-ready, with the capabilities you need to ship AI apps—fast. That’s why so many of the Fortune 500 trust MongoDB with their most critical workloads. Ready to think outside rows and columns? Start building at <a href="https://mongodb.com/Build" target="_blank">MongoDB.com/Build</a></li><li>Your host is Tobias Macey and today I'm interviewing Goutham Budati about his data-perspective-action framework for empowering data teams to be more influential in the business</li></ul> Interview   <ul><li>Introduction</li><li>How did you get involved in the area of data management?</li><li>Can you describe what the Data-Perspective-Action framework is and the story behind it?</li><li>What does it look like when someone operates at each of those three levels?<ul><li>How does that change the day-to-day work of an individual contributor?</li></ul></li><li>Why does technically excellent data work sometimes fail to drive decisions?<ul><li>How do you identify whether a data system or pipeline is actually creating value versus just existing?</li></ul></li><li>What's the moment when you realized that building reliable systems wasn't the same as enabling better decisions?<ul><li>Better decisions still need to be powered by reliable systems. How do you manage the tension of focusing on up-time against focusing on impact?</li></ul></li><li>What does it mean to add "Perspective" to data? How is that different from analysis or insights?</li><li>How do you know when you're overwhelming stakeholders versus giving them what they need?</li><li>What changes when you start designing systems to surface signal rather than just providing comprehensive data?</li><li>How do you learn what business context matters for turning data into something actionable?</li><li>What does it mean to design for Action from day one? How does that change what you build?<ul><li>How do you get stakeholders to actually act on data instead of just consuming it?</li></ul></li><li>Walk us through how you structure collaboration with business partners when you're trying to drive decisions, not just inform them.</li><li>What's the relationship between iteration and trust when you're building data products?</li><li>What does the transition from order-taker to strategic partner actually look like? What has to change?<ul><li>How do you position data work as driving the business rather than supporting it?</li></ul></li><li>Why does storytelling matter for data professionals? What role does it play that technical communication doesn't cover?</li><li>What organizational structures or team setups help data people gain influence?</li><li>Tell us about a time when you built something technically sound that failed to create impact. What did you learn?</li><li>What are the common patterns in dysfunctional data organizations? What causes the breakdown?</li><li>How do you rebuild credibility when you inherit a data function that's lost trust with the business?<ul><li>What's the relationship between technical excellence and stakeholder trust? Can you have one without the other?</li></ul></li><li>When is this framework the wrong lens? What situations call for a different approach?</li><li>How do you balance the demand for technical depth with the need to develop business and communication skills?</li><li>How should data professionals position themselves as AI and ML tools become more accessible?</li><li>What shifts do you see coming in how businesses think about data work?<ul><li>How is your thinking about data impact evolving?</li></ul></li><li>For someone who recognizes they're focused purely on the technical work and wants to expand their impact—where should they start?</li></ul> Contact Info   <ul><li><a href="https://www.linkedin.com/in/gouthambudati/" target="_blank">LinkedIn</a></li></ul> Parting Question   <ul><li>From your perspective, what is the biggest gap in the tooling or technology for data management today?</li></ul> Closing Announcements   <ul><li>Thank you for listening! Don't forget to check out our other shows. <a href="https://www.pythonpodcast.com" target="_blank">Podcast.__init__</a> covers the Python language, its community, and the innovative ways it is being used. The <a href="https://www.aiengineeringpodcast.com" target="_blank">AI Engineering Podcast</a> is your guide to the fast-moving world of building AI systems.</li><li>Visit the <a href="https://www.dataengineeringpodcast.com" target="_blank">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li><li>If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.</li></ul> The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA</a>

49 MIN

Data Engineering Podcast

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