The Multi-Engine Architecture: Stop Building Pipelines, Start Delivering AI

AI success is built on data interoperability. When teams cannot access data where it lives, they duplicate it; driving up costs, fragmenting governance, and disconnecting your data's meaning. The result? Inconsistent metrics and AI agents starved of the live, governed data they need to perform accurately and securely.

In this session, we will show you how to evaluate every layer of your architecture to break this cycle. We'll cover how Snowflake transformed its platform to be open and interoperable, and provide a practical blueprint to transform your own data estate. You will learn how to build semantic context, govern multi-catalog environments, and distribute data in-place—eliminating the copies and pipelines holding you back, without vendor lock-in.

Join us for the session. Walk away with an evaluation framework to start your transformation today.

What You'll Learn

• Master the Interoperability Framework: Learn to evaluate your architecture across the three critical layers—Data, Governance, and Semantics—so you can stop building pipelines just to move data.
• Standardize for Agentic AI: Build semantic context once and unify governance across multi-catalog and engine environments, ensuring your AI agents have access to the same governed, live data your BI teams rely on.
• Eliminate Data Sprawl: See how to access and distribute data in-place, enabling any engine to run on your data without duplicating it or creating vendor lock-in.