Snowflake announced advances that make data AI-enabled by design. This allows companies to rely on continuously available and managed data as AI moves from experimentation to real-world operational systems. New enhancements to Snowflake Postgres now allow databases to run natively in the AI Data Cloud, allowing enterprises to consolidate transactional, analytical, and AI use cases into a single secure platform.
To ensure that AI systems are trusted at enterprise scale, Snowflake is further building enhanced interoperability, governance, and resiliency capabilities into the platform, making Snowflake available to more customers directly wherever their data resides.
“As enterprises move from experimenting with AI to production, the real challenge is ensuring that AI systems have consistent access to data that is connected, managed, and discoverable across the enterprise,” said Christian Kleinerman, vice president of products at Snowflake.
“This means eliminating data silos, weak pipelines, and closed systems that slow AI adoption and increase risk. By bringing together unified operational and analytical data and open interoperability into one platform, we enable our customers to develop enterprise-ready AI systems that work with real business data securely and at scale,” Kleinerman continued.
“At Sigma, our customers expect live, interactive analysis of their latest business data,” said Jake Hannan, Head of Data, Sigma Computing. “Snowflake Postgres allows us to work directly with new transactional data within Snowflake without relying on complex pipelines or external systems. This gives our teams and customers a simpler, more reliable foundation on which to build managed analytics and AI-powered experiences that respond in real time.”
Connect enterprise data and AI to power mission-critical apps and AI agents
Most organizations still sile transactional and analytical databases into separate systems. This is a traditional approach that forces teams to rely on complex pipelines to connect these systems. This fragmentation significantly increases costs, delays development, introduces risk, and delays insight.
Snowflake Postgres eliminates these pipelines by consolidating transactional, analytical, and AI capabilities into a single enterprise-ready platform. Additionally, full compatibility with open source Postgres allows enterprises to migrate existing apps to Snowflake without changing code.
With Snowflake Postgres, teams can power critical apps and AI agents, use the latest data from operations to analyze business performance and trends, and build AI-driven capabilities like recommendations and predictions. All without the need for expensive, complex data pipelines or the infrastructure overhead of managing multiple vendors.
A set of PostgreSQL extensions that make it easy for Postgres to operate within an organization’s open, interoperable lakehouse built on Apache Iceberg, pg_lake enables enterprises to leverage Snowflake Postgres to directly query, manage, and write to Apache Iceberg tables using standard SQL.
This functionality is delivered within the familiar Postgres environment, allowing enterprises to eliminate costly data movement between transactional and analytical systems. Companies like BlueCloud and Sigma Computing use Snowflake Postgres to simplify their data architectures and run enterprise-ready AI and apps on top of connected data.
“For BlueCloud, Snowflake Postgres represents a huge opportunity to help our customers eliminate data pipelines without compromising performance,” said Rob Sandberg, SVP and Head of Advisory Consulting, BlueCloud. “Its enterprise-grade Postgres foundation provides real confidence, especially for the financial services organizations we support. Snowflake Postgres allows us to deliver low-latency transactional workloads alongside analytics and AI on a single platform, reducing overhead and helping our customers be more agile in achieving their business goals.”
Take control of your data and open it up for trusted AI
As AI moves into production, enterprises need data that is open, managed, and resilient as it flows between engines, formats, and environments. To address this need, Snowflake is expanding the way customers access, share, and manage their data, allowing AI systems to scale with real-world demands.
Ability to work freely between engines without affecting governance controls: To reduce silos and avoid vendor lock-in, Snowflake enables you to apply the same governance policies when Snowflake data is queried by other engines. Snowflake Horizon Catalog, which provides context and governance for AI across all data, enables customers like Merck, a science and technology company, and Motorq, a leading connected car intelligence company, to leverage external engines to securely access data in Apache Iceberg tables (now generally available), as well as create, update, and manage data stored in Iceberg tables (coming in public preview).
Seamless data collaboration across open formats: As organizations increasingly rely on open table formats, Snowflake simplifies how these formats can be shared without duplicating data or managing fragile pipelines. Open format data sharing extends Snowflake’s zero-ETL sharing model to include formats like Apache Iceberg and Delta Lake, enabling secure data sharing across teams, clouds, and geographies.
Customers can now share data in an open format while maintaining control over access and costs. A new integration with Microsoft OneLake (now generally available) provides mutual customers with secure bi-directional read access to Iceberg data managed by Snowflake or Microsoft Fabric. This means customers can seamlessly access all their data across both platforms without complexity or data duplication.
Built-in resiliency to protect your business-critical data: To help businesses address regulatory requirements and withstand disruption, Snowflake is strengthening the way data is protected by default. Snowflake Backups (now generally available) further enhance data resiliency by protecting your business-critical data. Organizations can recover faster from ransomware and disasters while ensuring that data is not modified or deleted after it is created. These protections give businesses greater confidence that their critical data will be preserved even in the face of unexpected events or security incidents.
