What Context Really Means in Data Engineering and AI

Summary In this episode Soham Azumdar, co-founder and CEO of Wisdom.ai, talks about what “context” really means in data engineering and AI systems. He explores why context has become such an overloaded term, spanning everything from semantic layers and data catalogs to tribal knowledge, query logs, dashboards, and even agent memory. Soham explained that the big shift is that context is no longer being prepared primarily for human analysts, but for LLMs and agents that can’t reliably fill in missing gaps on their own. That change raises the bar for how context is represented, validated, benchmarked, and maintained so that AI systems can produce trustworthy outcomes. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm interviewing Soham Mazumdar about what "context" actually means in data engineering Interview Introduction How did you get involved in the area of data management? One of the perennial challenges of engineering in all forms is building a shared understanding of what a given word means. "Context" is one that is being used for an increasing number of purposes with the introduction of AI agents. Can you start by sharing some of the ways that this terminology overload has caused problems in your own experience? Data engineering has arguably always been about context engineering, but at the scale of human consumers. What are the substantive changes that AI/agentic consumers bring to the discipline? While we all understand the notion of "context", turning it into a useful and re-usable component is a different matter entirely. What are some of the ways that "business context" or "technical context" manifests as a tangible artifact? This also brings up the question of data modeling. What are some of the key attributes that are necessary when storing, enriching, evolving, and joining into that context? One could argue that the entire history of data warehousing is about building organizational context. What are the real differences in approach for today's work of capturing and activating that context? How does your work at Wisdom AI address the technical and operational burdens of capturing, modeling, and exposing context at the speed necessary to keep up with organizational demands? What are the most interesting, innovative, or unexpected ways that you have seen Wisdom AI used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Wisdom AI/context engineering? When is Wisdom AI the wrong choice? What do you have planned for the future of Wisdom AI? 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 [email protected] with your story. Links Wisdom AI Context Engineering Knowledge Graph Ontology Snowflake Open Semantic Interchange (OSI) Semantic Layer Palantir Foundry The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA