Join Atlan atDatabricks Data +AI Summit

Everyone's talking about context layers. Nobody agrees on what one is — a semantic layer with a better name? A knowledge graph in a trench coat? AI is moving from pilot to production — and it made data teams into janitors: cleaning up hallucinations, debugging agents. But the context agents need most is what data teams already own. The definitions, the lineage, the institutional knowledge. That's not a cost center. It's your most strategic AI asset.

You already have Databricks — maybe Unity Catalog too. But your agents still don't know which "revenue" definition to use, which tables to trust, or what the business actually means. The gap between pilot and production isn't a model problem. It's a context problem.

Join Prukalpa Sankar to see it live: dark Databricks tables become AI-ready — automated lineage mapped, AI-bootstrapped definitions added, Context Quality Score green — and a Genie query returns a governed response via MCP. All live. All on stage.

At Mastercard, trust has always been the product. For six years, that trust has been engineered in layers: privacy by design, then data by design, and now context by design. Each evolution built on the last.

Now, as AI agents begin transacting at the speed of commerce, the latest layer matters most — an Enterprise Context Layer, built on Databricks, that machines can read, reason over, and act on without human intervention at every step.

Join Vivek Radhakrishnan and Brian Piel of Mastercard, and Austin Kronz of Atlan, to learn how engineering and enablement co-own context as a dual mission (and why the productive tension between them is the reason governance actually sticks), how automated metadata and human-centered product thinking combine to build AI-ready data products on Databricks, and how this foundation sets the stage for text-to-SQL and conversational search — and every AI use case that follows.