Snowflake Announces New Open Framework for Enterprise Data and AI Interoperability

Snowflake delivers interoperability across modern data architectures without compromising governance or flexibility. This enables data to be used securely and consistently based on a single, governed logical data foundation.

Embedding Interoperability into Design:

Snowflake provides an environment where data can be seamlessly utilized across clouds, tools, and engines through general availability support for Apache Iceberg v3, the broadest functional support in the market, and Snowflake Storage for Apache Iceberg Tables. This creates a comprehensive, interoperable platform for managing Apache Iceberg while avoiding vendor lock-in.

Universal Governance at Scale:

With the Snowflake Horizon Catalog powered by Apache Polaris, Snowflake enables bidirectional Iceberg interoperability. This allows governance capabilities to be applied centrally, securely, and consistently across all data and platforms.

Supporting Enterprise AI:

Companies including Affirm, Indeed, NTT Docomo, and Samsung Ads are using Snowflake to simplify their data architecture and build AI on a consistent and reliable foundation.

Snowflake (NYSE: SNOW), the AI Data Cloud company, today at Snowflake Summit 26 announced new interoperability capabilities for the AI era. These capabilities allow enterprises to seamlessly access, govern, share, and utilize data across disparate systems without constraints or compromises.

For the first time, enterprises can leverage data not only within Snowflake but also data residing in external data lakes or open systems as a single, live, governed copy without moving or duplicating it. With the Snowflake Horizon Catalog, organizations can transform siloed data into an integrated data foundation optimized for AI, providing a secure and controlled environment for users and AI agents to discover and utilize data across the entire enterprise context.

Snowflake is promoting open interoperability that seamlessly handles data inside and outside Snowflake through support for Apache Iceberg v3 and the provision of Snowflake Storage for Apache Iceberg Tables. This minimizes unnecessary data movement and enables data utilization across multiple environments. Additionally, the Horizon Catalog powered by Apache Polaris enables bidirectional read/write access from external engines to Snowflake-managed Iceberg. Furthermore, Snowflake can apply consistent governance across the open ecosystem through support for External Engine Access Management and the Iceberg REST Scan Plan API. This maintains fine-grained access control and security policies across supported engines.

Christian Kleinerman, Executive Vice President of Product at Snowflake, said:

"Many companies still rely on moving or copying data to make it usable. However, this approach cannot keep pace with the speed of AI evolution. As innovation accelerates, data fragmentation itself becomes a major constraint. We are fully committed to interoperability and openness. With Snowflake's new capabilities, companies can directly utilize data wherever it resides, with a single, unified governance framework. Furthermore, by eliminating data duplication and establishing common business definitions through semantic views, we create a consistent and reliable data foundation for both humans and AI agents."

Vivek Anandpara, Vice President of Engineering at Affirm, said:

"At Affirm, having a clear and consistent view of our data is critical to providing transparent and responsible financial services. Snowflake allows us to utilize data across multiple systems without duplication and apply consistent governance across the environment. This enables our teams to make faster decisions, improve operations, and safely scale AI on a trusted data foundation. We have proven this by migrating thousands of tables and critical financial workloads to Polaris using Snowflake's interoperable, governed data foundation. Snowflake worked closely with us to achieve a zero-downtime migration with accuracy at scale."

Open and Multi-Engine Data Utilization with an Interoperable Lakehouse for the AI Era

As enterprises scale their AI initiatives, traditional architectures increase complexity and drive up costs. Data is scattered across multiple platforms and operational systems, forcing many companies to spend significant time and effort on copying, integrating, and verifying consistency before they can use it. This operational burden not only slows down AI adoption but also creates inconsistent data foundations, making it difficult for AI systems to produce reliable results.

Snowflake removes these barriers by providing an environment where data can be utilized directly where it resides, without moving or copying it. By combining open connectivity, intelligent query capabilities, and support for open standards, it creates a single data foundation for accessing, understanding, sharing, and utilizing all data.

The new interoperability features announced today enable enterprises to:

Promote Open Standard Support with Apache Iceberg:

With the general availability of Apache Iceberg v3, Snowflake has achieved broad support for the latest open table format technology. This includes support for more diverse data, cross-system change data capture, and high-performance processing of semi-structured data. This eliminates fragmented data architectures and reduces costly data movement across multiple platforms and engines.