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In a move poised to reshape the interaction between real-time applications and analytics, TigerData today unveiled Tiger Lake, a new architectural layer designed to unify Postgres and lakehouse infrastructure. This innovative solution eliminates the burden of data pipelines and vendor lock-in, providing a sense of ease and freedom for data engineers and architects.
The launch marks a strategic leap for TigerData, the team behind Tiger Postgres and TimescaleDB, as it positions itself at the center of a growing demand for composable, open data systems that serve both real-time operations and deep analytics.
Tiger Lake is designed to enable bidirectional data flow between Tiger Postgres and Iceberg-backed lakehouses, eliminating the need for fragile syncs or deferred ETL jobs. According to Mike Freedman, co-founder and CTO of TigerData, the product was built to meet a clear and growing need in the industry.
“Postgres has become the operational heart of modern applications, but until now, it’s existed in a silo from the lakehouse,” Freedman said. “With Tiger Lake, we’ve built a native, bidirectional bridge between Postgres and the lakehouse. It’s the architecture we believe the industry has been waiting for.”
The approach of Tiger Lake stands in stark contrast to existing systems that force developers to choose between transactional speed and analytical depth. Tiger Lake offers both in real-time, by leveraging Tiger Postgres for ingestion, transformation, and real-time rollups, while tapping into Iceberg for historical insights and ML features. This superiority in approach is set to revolutionize the industry.
For companies that have long struggled with brittle streaming architectures, Tiger Lake offers a simpler, native solution, promising to alleviate these challenges.
“We stitched together Kafka, Flink, and custom code to stream data from Postgres to Iceberg—it worked, but it was fragile and high-maintenance,” said Kevin Otten, Director of Technical Architecture at Speedcast. “Tiger Lake replaces all of that with native infrastructure. It’s not just simpler—it’s the architecture we wish we had from day one.”
Tiger Lake’s bidirectional sync capability means that computed insights, such as semantic summaries, aggregates, or ML features, can be returned from the lakehouse into Postgres, where they’re readily available for agents, dashboards, or applications. This bridges the gap between live operational context and deep analytical history, powering everything from intelligent agents to customer-facing analytics.
At the core of Tiger Lake’s value proposition is its open, composable foundation. It integrates directly with AWS S3 Tables and uses Apache Iceberg as the data format, allowing developers to maintain control over their data architecture.
Innovative brands such as Speedcast, Monte Carlo, and others are already deploying Tiger Lake in production, starting with native support for AWS S3 Tables.
Tiger Lake stands in opposition to closed, monolithic data stacks. Instead of boxing developers into proprietary runtimes, it enables integration with a range of tools, from Spark to Snowflake, while preserving operational speed and data ownership.
Tiger Lake is now in public beta on Tiger Cloud. Initial capabilities include streaming Postgres and TimescaleDB hypertables into S3 Tables and syncing data back into Postgres. Planned enhancements will enable developers to query Iceberg catalogs directly from within Tiger Postgres, establishing full round-trip workflows.
According to TigerData, these capabilities will allow developers to build “real-time and intelligent applications—without giving up control or composability.”
Founded in 2017, TigerData is the company behind TimescaleDB and Tiger Postgres. An AWS Partner with more than 2,000 customers (including Warner Music, HuggingFace, Mistral, Linktree, and Postman), TigerData has raised over $180 million from Benchmark, NEA, Redpoint, and Tiger Global. With the release of Tiger Lake, it positions itself as a key enabler of the next generation of data-driven applications.
