Snowflake Integrates Postgres for Unified Apps, Analytics, and AI
Snowflake is introducing Snowflake Postgres, a new solution designed to bridge the gap between transactional applications and analytical workloads, eliminating the need for complex ETL pipelines. Announced features include data mirroring for real-time replication and 'Postgres for your data lake' for flexible data synchronization with open formats like Iceberg. These capabilities aim to provide businesses with consistent, up-to-date data for AI and real-time applications, impacting teams managing both OLTP and OLAP systems.
- →Data Mirroring for real-time Postgres to Snowflake replication
- →Postgres for Data Lake with open format integration
- →Unified data platform to reduce ETL pipeline complexity
- →Improved data synchronization performance for financial institutions
- →Streamlined data consolidation for Ericsson's global operations
Features (2) ›
- Data Mirroring for real-time Postgres to Snowflake replication
Data mirroring provides low-latency replication between Postgres and Snowflake, automatically maintaining target tables that reflect source changes, including schema updates. It offers zero infrastructure management, always-fresh reads via a '$liveview', transactional consistency, built-in change history, and high throughput. This feature is currently in public preview soon and will later support Snowflake-to-Postgres mirroring.
- Postgres for Data Lake with open format integration
This feature allows for flexible data movement and synchronization between Postgres and Snowflake using open formats like Iceberg, Parquet, and CSV. It supports moving files, creating shared open-format tables, and applying SQL transformations during data movement. This provides more control over which data moves and when, with general availability expected soon.
Enhancements (2) ›
- Improved data synchronization performance for financial institutions
SimCorp utilized Snowflake Postgres to achieve a 10x reduction in data synchronization time for its market ID service, decreasing it from hours to under 20 minutes. This improvement supports mission-critical risk models and credit spread curves with immediate availability for global clients.
- Streamlined data consolidation for Ericsson's global operations
Ericsson consolidated data from four legacy databases into Snowflake Postgres, eliminating complex sync pipelines that previously caused up to 40-day lags. This significantly reduced data lag for their customer support platform from 48 hours to under an hour, ensuring all teams access the same trusted data.
Notes (1) ›
- Unified data platform to reduce ETL pipeline complexity
Snowflake Postgres aims to unify transactional and analytical data, addressing a common infrastructure pain point for enterprises. By offering seamless connections, it reduces the costs and risks associated with traditional ETL, batch jobs, and third-party tools, enabling faster decision-making and supporting real-time applications and AI.
https://www.snowflake.com/content/snowflake-site/global/en/blog/postgres-data-mirroring