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The evolution of the enterprise data stack is accelerating with the rise of agentic AI, demanding unified governance and open access to all data. Snowflake is addressing this need by announcing a suite of innovations that redefine the enterprise lakehouse for the AI era. These advancements focus on making it easier for organizations to ingest, access, and govern data across its entire lifecycle. With new capabilities in Snowflake Horizon Catalog, Snowflake Openflow, and the introduction of Snowflake Postgres, the platform enables enterprises to connect disparate data sources, providing a governed, interoperable foundation for building and powering intelligent AI agents and applications.
Snowflake announced major enhancements to its enterprise lakehouse platform.
Horizon Catalog provides unified governance for data across all sources and formats.
Snowflake Openflow (GA) automates data integration from virtually any source.
Interactive Tables and Warehouses (coming soon) deliver sub-second analytics.
Snowflake Postgres (coming soon) brings transactional data onto the platform.
These innovations break down data silos to fuel agentic AI with governed data.
A primary obstacle to successful AI implementation is fragmented data governance across siloed systems. Snowflake's Horizon Catalog directly addresses this by providing a universal AI catalog with a unified security and governance framework that secures data across every region, cloud, and format—without vendor lock-in. It is designed for interoperability, working seamlessly with native Snowflake objects, open table formats like Apache Iceberg and Delta Lake, and data in relational databases. “With advancements to Horizon Catalog, we’re giving enterprises context and governance for AI across all their data by default — wherever it lives, and without vendor lock-in,” said Christian Kleinerman, EVP of Product at Snowflake.
As AI raises expectations for speed, Snowflake is introducing capabilities to deliver immediate, data-driven experiences. Interactive Tables and Warehouses (generally available soon) provide low-latency, high-concurrency analytics, enabling teams to uncover insights in sub-seconds. Building on this, near real-time streaming analytics (now in private preview) allows organizations to act on live data within seconds using familiar tools. This end-to-end solution supports mission-critical use cases like fraud detection and personalization by combining live data streams with historical context, moving customers beyond traditional batch processing.
A significant architectural roadblock has been the separation of transactional and analytical data. Following its acquisition of Crunchy Data, Snowflake is introducing Snowflake Postgres (public preview soon), a fully-managed service that brings the popular Postgres database onto its platform. This integration allows developers to work with transactional data within the same secure foundation as their analytics and AI, eliminating costly data movement and enabling the development of AI agents and intelligent apps directly on operational data. The company is also open-sourcing pg_lake, extensions that let developers query and manage Apache Iceberg tables directly from Postgres.
Snowflake's latest innovations represent a holistic push to consolidate and govern the modern data ecosystem. By unifying governance with Horizon Catalog, automating integration with Openflow, and bridging the gap between transactional and analytical systems with Snowflake Postgres, the platform is providing the essential, governed data foundation required for enterprises to successfully build and scale agentic AI applications that drive tangible business value.
Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 12,000 customers around the globe, including hundreds of the world’s largest companies, use Snowflake’s AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone.
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