Snowflake, the AI Data Cloud company, has unveiled a series of strategic advancements designed to make enterprise data inherently AI-ready, enabling organizations to deploy AI systems at scale with confidence. The company introduced key updates to Snowflake Postgres, which will be generally available soon, allowing the world’s most widely used database to run natively within the AI Data Cloud. This integration enables enterprises to consolidate transactional, analytical, and AI workloads on a single, secure, and governed platform. Christian Kleinerman, EVP of Product at Snowflake, emphasized that as AI shifts from experimentation to real-world production, the core challenge lies in ensuring consistent access to data that is connected, discoverable, and governed across the enterprise. By eliminating data silos, fragile pipelines, and closed systems, Snowflake aims to accelerate AI deployment while reducing risk. Snowflake Postgres leverages pg_lake, a set of PostgreSQL extensions that enable seamless interaction with Apache Iceberg tables—a high-performance format for large-scale analytics. This allows teams to query, manage, and write data directly using standard SQL, all within a familiar Postgres environment. This capability eliminates the need to move data between systems, reducing latency and infrastructure complexity. Companies like Sigma Computing and BlueCloud are already leveraging Snowflake Postgres to simplify their data architectures. Jake Hannan, Head of Data at Sigma Computing, noted that the platform allows teams to work directly with up-to-the-minute transactional data without relying on complex pipelines, enabling faster, more reliable analytics and AI-powered experiences. To support trusted AI at scale, Snowflake is enhancing data governance, interoperability, and resilience. The company now enables consistent governance policies across different data engines, ensuring that data remains secure and compliant even when accessed from external systems. Snowflake Horizon Catalog provides unified context and governance for AI across all data, supporting customers like Merck and Motorq in securely accessing and managing data in Apache Iceberg tables. Snowflake also expanded its open data sharing capabilities through Open Format Data Sharing, which extends its zero-ETL model to support Apache Iceberg and Delta Lake. This allows secure, efficient data sharing across teams, clouds, and regions without duplication. A new integration with Microsoft OneLake enables bidirectional read access for Iceberg data managed by Snowflake or Microsoft Fabric, simplifying cross-platform data workflows. In addition, Snowflake Backups—now generally available—enhances data resilience by protecting critical data from ransomware and accidental deletion. Data is preserved by default, ensuring recoverability and integrity even during disruptions. These innovations reflect Snowflake’s broader vision of an AI Data Cloud where data and AI work together seamlessly, securely, and at scale. With over 12,000 global customers, including many of the world’s largest enterprises, Snowflake continues to lead in helping organizations unlock the full potential of their data in the age of artificial intelligence.