TigerData, the creator of TimescaleDB and Tiger Postgres, has announced Tiger Lake, a new architecture designed to address one of the most persistent problems in modern data infrastructure: connecting fast-moving operational databases with scalable lakehouses without the complexity of pipelines or vendor lock-in.
Tiger Lake makes Postgres the engine behind both operational and analytical workloads by establishing a native, bidirectional sync between Tiger Postgres and Iceberg-backed lakehouses, such as those running on AWS S3.
“Postgres has become the operational heart of modern applications, but until now, it’s existed in a silo from the lakehouse,” said Mike Freedman, co-founder and CTO of TigerData. “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.”
What Tiger Lake Offers
Tiger Lake isn’t a feature or integration; it’s a foundational architecture for real-time, intelligent applications. It allows data to move freely in both directions between Postgres and the lakehouse, enabling fast ingestion and deep insights without the need for redundant systems or brittle orchestration.
Key capabilities include:
- Bidirectional sync between Postgres and Iceberg: Real-time operational data flows into the lakehouse, while analytical results, such as ML features or semantic summaries, can be returned to Postgres for immediate use in applications.
- Streaming without ETL: Native replication from Postgres to AWS S3 Tables eliminates the need for Kafka, Flink, or manual glue code.
- Real-time and historical analytics in one system: Use Tiger Postgres for low-latency, high-ingest workloads and Iceberg for scale-out querying without duplicating data pipelines.
- Built on open standards: Tiger Lake is fully compatible with Apache Iceberg and AWS S3 Tables, avoiding proprietary formats and ensuring ecosystem compatibility.
- Production-ready infrastructure: Tiger Lake builds on Tiger Postgres, which extends PostgreSQL with TimescaleDB to support agentic apps, rollups, and concurrent queries at scale.
Real Users, Real Impact
For organizations struggling to maintain complex streaming architectures, Tiger Lake offers a path to simplification.
“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.”
Whether powering real-time dashboards, copilots, or ML pipelines, Tiger Lake enables teams to utilize Postgres as a live surface for intelligence that is no longer limited to operational workloads alone. Innovative brands such as Speedcast, Monte Carlo, and others are already deploying Tiger Lake in production, starting with native support for AWS S3 Tables.
Built for Openness, Not Lock-In
In contrast to vertically integrated platforms that control the full data stack, Tiger Lake promotes modularity and flexibility. By connecting Postgres directly to Iceberg via open AWS S3 Tables, TigerData lets developers choose the right components for their workloads.
This philosophy marks a sharp contrast to all-in-one platforms that fuse storage, compute, and query under a proprietary layer.
Now in Public Beta
Tiger Lake is now available in public beta on Tiger Cloud, with initial capabilities focused on streaming Postgres tables and TimescaleDB hypertables to AWS S3 Tables using the Iceberg format. It also supports streaming data from S3 back into Postgres, allowing for tight operational-analytical integration out of the box. TigerData’s roadmap includes deeper functionality such as querying Iceberg catalogs directly from within Tiger Postgres and full round-trip workflows, where computed insights from the lakehouse (such as aggregates and ML features) can be synced back into Postgres for real-time application use.
By collapsing the gap between fast, real-time systems and scalable data lakes, TigerData’s Tiger Lake may represent a new default for modern data architecture: real-time, open, and deeply composable.