Snowflake announced a big milestone now. They released the public preview of Snowflake Postgres. This is a fully-managed database service that works with PostgreSQL and runs on the Snowflake platform. This launch is a major step in merging transactional and analytical tasks in Snowflake’s AI Data Cloud. It helps businesses combine data operations and speed up app development. Bringing Postgres into Snowflake helps organizations run operational databases. This allows for analytics and AI workloads. This cuts down on costly data movement and simplifies modern application setups. Snowflake Postgres offers a familiar Postgres experience. It runs in Snowflake’s secure, governed, and scalable environment.
By bringing Postgres into Snowflake, organizations can now run operational databases alongside analytics and AI workloads eliminating costly data movement and simplifying modern application architectures. Snowflake Postgres delivers a familiar Postgres experience from within Snowflake’s secure, governed, and scalable environment.
Enterprise-Ready Postgres Built for Scale
Snowflake Postgres supports heavy transactional workloads. It is fully compatible with PostgreSQL tools and extensions. Teams can easily lift and shift existing applications. They can also use popular Postgres clients, ORMs, and libraries without changing the code.
The public preview release offers capabilities that enterprises need for production-like workloads, including:
- Full Postgres compatibility
- Simple provisioning and instance resizing
- Minor and major Postgres version upgrades
- Connection pooling support
- Read replicas for scaling
- Built-in disaster recovery and point-in-time recovery
- Maintenance windows
- Database logs and instance metrics
- Postgres insights dashboards
- Network controls including ingress/egress and PrivateLink
- Customer-managed encryption keys
These features provide developers with operational flexibility while maintaining stringent governance and security controls.
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A Message from Snowflake’s Engineering Leadership
Craig Kerstiens, a prominent voice in the Snowflake developer community, highlighted the significance of this release: “Snowflake Postgres is now in public preview, bringing fully compatible, production-ready Postgres directly onto Snowflake. Yes, your operational database sitting right alongside your analytics. Snowflake Postgres is built on everything customers loved with Crunchy Bridge, a really reliable, performant, even ‘boring’ if you will database that didn’t create fuss for you. It got out of your way so you didn’t have to think about it and could build and deliver value for your customers, and now that same experience is available on Snowflake.
What’s included today in public preview:
- Full Postgres compatibility
- Simple provisioning + instance resizing
- Minor & major Postgres upgrades
- Connection pooling
- Read replicas
- Built-in DR + point-in-time recovery
- Maintenance windows
- Database logs & instance metrics
- Postgres insights
- Network ingress/egress controls + PrivateLink
- Customer-managed keys
All of that is available now. Give it a try, let me know what you think. This is just the beginning. Oh, and because you’re probably wondering. Pg_lake is coming very soon to Snowflake Postgres.
This endorsement underscores Snowflake’s commitment to delivering enterprise-grade Postgres capabilities without compromising on reliability or developer experience.
Why This Matters for Data-Driven Enterprises
Snowflake Postgres addresses a long-standing challenge in modern data architectures: the need to operate transactional (OLTP) and analytical (OLAP) systems without siloed data pipelines.
By unifying both database types on a single platform, Snowflake enables:
- Simplified architectures with fewer moving parts
- Reduced operational overhead and costs
- Real-time data availability for analytics and AI
- Faster time to value for data applications
For organizations building AI-driven applications and intelligent services, this means access to fresh transactional data within the same environment used for analytics and machine learning removing latency and synchronization challenges that traditionally slow innovation.
