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.

For AI agents, data fragmentation and duplication can be catastrophic. It forces agents to repeatedly search and reconstruct semantic understanding, wasting tokens and increasing costs per thought. And when the same data lives in multiple places, agents can return incorrect or hallucinated answers, putting data trust at risk and ultimately undermining business decisions — a nonstarter for enterprise readiness. 

But Snowflake understands: AI requires full interoperability.

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.

In this practical guide, you’ll read about:

• Connecting data without compromise: Learn how open table formats like Apache Iceberg serve as the connective tissue for your data estate, helping you avoid functional lock-in to bring your tools to the data.
• Streamlining for scale: Discover how declarative data engineering, zero-copy cloning and automated ingestion can reduce infrastructure headaches and move your team closer to ZeroOps.
• Governing for the AI era: Understand how a unified approach to governance, security and trust shifts data teams from reactive cleanup to proactive protection.
• Real-world architectural patterns: See modern lakehouse architectures, highlighting Snowflake's interoperability with major cloud partners like AWS, Microsoft Azure and Google Cloud.
• Proven business cases: Read how industry leaders Goldman Sachs, Affirm and Indeed are already realizing ROI by modernizing their data foundations with lakehouses built on Snowflake.

An interoperable lakehouse doesn’t just update your data stack; it reshapes what your business can do with data. It replaces sprawling systems with a foundation that is connected, open and accessible without data movement. The organizations highlighted in this ebook aren’t exceptions — they’re harbingers of what is possible when data architecture stops being a constraint and starts being a catalyst.

Faster access to high-quality data, universal governance and a meaningful drop in the everyday friction that stands in the way of progress. These are not abstract promises but tangible results. It’s time to move your AI initiatives from idea to impact because of the architectural framework, not in spite of it.