Building the Interoperable Lakehouse: Data Strategies for AI Leaders

Architect for agency over your data. Use any engine and accelerate AI with a lakehouse built with Snowflake-simple interoperability

As businesses today move beyond AI experimentation and onto production, many are finding their greatest constraints aren’t so much models but the underlying data architecture those models rely on.

It’s a problem of fragmentation, stemming from limited interoperability across engines and tools. When teams can’t work with data where it lives, they resort to costly, labor-intensive architectures that rely on copying data — which only contributes to tool sprawl, disconnected governance, and higher engineering and storage costs.

In Building the Interoperable Lakehouse, we introduce a better way. Discover how to design a lakehouse architecture, grounded on Apache Iceberg™ and Apache Polaris™, that increases AI readiness while lowering engineering effort and costs.

This guide provides foundational understanding of open table formats, different approaches to architecting your lakehouse, and why it’s so important to be able to act on your data for any operation from any engine.