Snowflake Postgres and Enhanced Data Governance Features
Snowflake has introduced Snowflake Postgres, a significant addition to its AI Data Cloud, accompanied by a comprehensive suite of updates aimed at enhancing data governance, sharing, and resilience. These advancements are crafted to prepare enterprise data for the demands of artificial intelligence as organizations transition from experimentation to production environments.
Running natively within the AI Data Cloud, Snowflake Postgres is poised for general availability in the near future. This strategic release is designed for customers seeking to unify transactional workloads, analytics, and AI development within a single cohesive environment.
“As businesses move from AI experimentation to production, the real challenge is ensuring AI systems can consistently access data that is connected, governed, and discoverable across the enterprise,”
noted Christian Kleinerman, EVP of Product at Snowflake.
Snowflake emphasizes that many organizations still operate with a separation between transactional databases and analytical systems, a division that complicates data pipelines and escalates costs while introducing operational risks.
Postgres Push
Snowflake Postgres aims to seamlessly integrate Postgres-style transactional operations with analytics and AI functionalities. The platform promises full compatibility with open-source Postgres, allowing customers to migrate existing applications to Snowflake without necessitating code modifications.
Additionally, Snowflake Postgres will incorporate Apache Iceberg through pg_lake, a set of PostgreSQL extensions. This integration enables users to query, manage, and write to Iceberg tables using standard SQL from a Postgres environment, effectively minimizing data movement between transactional and analytical systems. Customers can maintain their data in open table formats while leveraging Snowflake for database operations and analytics.
BlueCloud and Sigma Computing have already adopted Snowflake Postgres, recognizing its potential in scenarios that merge operational applications with analytics and AI models reliant on real-time operational data.
“At Sigma, our customers expect live, interactive analytics on the most current business data,”
stated Jake Hannan, Head of Data at Sigma Computing.
“With Snowflake Postgres, we can work directly on fresh transactional data inside Snowflake without relying on complex pipelines or external systems. That gives our teams and customers a simpler, more reliable foundation to build governed analytics and AI-powered experiences that respond in real time.”
BlueCloud highlighted the advantages of Snowflake Postgres for financial services workloads, particularly in merging low-latency transactions with analytics.
“For BlueCloud, Snowflake Postgres represents a major opportunity to help our customers eliminate data pipelines, without compromising performance,”
commented Rob Sandberg, SVP and Head of Advisory Consulting at BlueCloud.
“Its enterprise-grade Postgres foundation brings real credibility, particularly for the financial services organizations we support. With Snowflake Postgres, we can deliver low-latency transactional workloads alongside analytics and AI on a single platform, reducing overhead and helping our customers be more agile in meeting their business goals.”
Catalog Controls
In tandem with the launch of Snowflake Postgres, the company has unveiled product updates that enhance data interoperability and governance. Recognizing that AI deployments necessitate data that is both governed and resilient across various systems and formats, Snowflake has expanded its enforcement of governance policies when querying Snowflake data from external engines.
This initiative is linked to the Snowflake Horizon Catalog, which provides context and governance for AI across data. The Horizon Catalog will enable customers to access data in Apache Iceberg tables using external engines, with this support now generally available. Furthermore, customers will have the ability to create, update, or manage data stored in Iceberg tables, with this feature currently in public preview.
Merck and Motorq are among the customers leveraging Horizon Catalog for improved access and governance across diverse systems and formats, as Snowflake aims to mitigate data silos and address concerns related to vendor lock-in.
Open Sharing
Snowflake has also announced Open Format Data Sharing, extending its “zero-ETL sharing model” to encompass open formats such as Apache Iceberg and Delta Lake. This feature facilitates secure data sharing across teams, clouds, and regions while maintaining stringent control over access and costs.
Additionally, Snowflake has achieved a generally available integration with Microsoft OneLake, allowing mutual customers to benefit from secured bidirectional read access for Iceberg data managed by either Snowflake or Microsoft Fabric. This integration aims to eliminate data duplication and simplify operations for customers utilizing both platforms.
Backup Option
Snowflake has made Snowflake Backups generally available, positioning this feature as a safeguard for business-critical data and a means to comply with regulatory requirements. The design of this backup solution ensures that data remains unaltered or deleted once created, facilitating quicker recovery in the event of ransomware attacks or other disruptions.
These updates reflect Snowflake’s broader commitment to embedding interoperability, governance, and resilience into its platform, empowering customers to utilize Snowflake alongside data stored in various locations.
