Snowflake has open-sourced its PostgreSQL extensions, enabling developers and data engineers to integrate the popular open-source database directly with its lakehouse system. The pg_lake extension facilitates direct reading and writing to Apache Iceberg tables, reducing the need for data extraction and movement and allowing PostgreSQL to act as an interface for managing an open lakehouse, thereby lowering the barrier for PostgreSQL teams to adopt Snowflake.
Cloud data platform vendor Snowflake has made its set of PostgreSQL extensions open source in a bid to help developers and data engineers integrate the popular open source database with its lakehouse system.
would allow developers and data engineers to read and write directly to Apache Iceberg tables from PostgreSQL, thereby cutting out the need to extract and move data. Iceberg is an open table format that advocates say allows users to bring their preferred analytics engines to their data without moving it; it is backed by Snowflake, Google, AWS and others.that making the extension open source would allow developers who use PostgreSQL in their stack to turn the database into an interface to manage an open lakehouse. The lakehouse concept wasKleinerman said: “One of the most common use cases for developers to build applications against PostgreSQL and then or copy the data for analytics into either a data platform like Snowflake or increasingly, an open data lakehouse like Iceberg tables on S3 Tables in or Microsoft Onelake … that data now becomes available for analytics.”, Craig Kerstiens, Snowflake software engineering director, said pg_lake would allow developers to manage Iceberg tables directly in PostgreSQL by introducing a new Iceberg table type where PostgreSQL acts as the catalog. It would also allow developers to query raw data files in the data lake or external Iceberg tables, Delta tables, and various geospatial file formats from PostgreSQL. Robert Kramer, Moor Insights & Strategy vice president and principal analyst, said giving PostgreSQL users a direct path into Snowflake’s lakehouse and AI capabilities without forcing architectural disruption is a smart move. “Most organizations are not ripping out PostgreSQL — and Snowflake clearly understands that. Pg_lake lowers the barrier for PostgreSQL teams to gradually adopt Snowflake for high-value analytics and automation, rather than treating it as an all-or-nothing platform decision. I anticipate incremental adoption, but real traction over time, especially as teams blend operational databases with governed AI execution.”'It isn't designed to solve privacy concerns,' Grafana CTO says of Bring Your Own Cloud Snowflake also announced the general availability of Snowflake Intelligence, an AI agent it says offers users the ability to answer complex questions in natural language and put insights at every employee’s fingertips. It has made additions to its Horizon data catalog too. Across the board, Kramer said Snowflake may still need to improve in terms of scale, monitoring, and real-world costs for agent workloads. “Buyers might need some help understanding how Snowflake is different from Databricks and other cloud platforms. Snowflake is designed to be a platform where AI can work reliably and responsibly, not just for testing purposes. For customers who want to move from experimenting with AI to using it in real-world operations, this mindset is really important.” ® Microsoft apologizes for not explaining cheaper no-AI M365 plans, and all it took was a government lawsuitUncle Sam lets Google take Wiz for $32B
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