Logical First, Physical Second: A Pragmatic Path to Trusted Data
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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 Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 dataengineeringpodcast.com/retool 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. 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 MongoDB.com/Build 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 dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud. 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 Interview Introduction How did you get involved in the area of data management? Can you start by giving your definition of "data architecture" and what it encompasses? 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) 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? 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? The current stage is largely defined by the ELT pattern. What are some of the ways that workflow can encourage shortcuts? 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? 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? 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? What are the most interesting, innovative, or unexpected ways that you have seen teams approach the creation and implementation of their data architecture? What are the most interesting, unexpected, or challenging lessons that you have learned while working in data architecture? 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? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Idera ER Studio ELT RDF == Resource Description Framework ORM == Object-Relational Mapping The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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