TigerData, the innovative team behind TimescaleDB and Tiger Postgres, has introduced a transformative architectural layer known as Tiger Lake. This advancement redefines data infrastructure by merging the operational speed of Postgres with the analytical capabilities of the lakehouse. With the launch of Tiger Lake, a plethora of new opportunities emerges, effectively addressing the challenge of integrating live application data with deeper analytical insights, all while avoiding the pitfalls of brittle ETL processes, vendor lock-in, and technical compromises.
Rethinking the Data Stack
Tiger Lake transforms Postgres into a dynamic real-time engine, facilitating seamless connections to Iceberg-backed lakehouses. This architecture enables continuous, bidirectional data movement between operational databases and scalable cloud storage systems. Mike Freedman, co-founder and CTO of TigerData, remarked, “Postgres has become the operational heart of modern applications, but until now, it’s existed in a silo from the lakehouse. 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.”
Unified Architecture Without Pipelines
At its essence, Tiger Lake dismantles the conventional barriers separating transactional systems from analytical ones. By eliminating the need for data duplication across layers or reliance on intricate pipelines, developers can leverage Postgres and Iceberg as integral components of a cohesive system. Postgres serves for rapid ingestion and transformation, while Iceberg excels in handling historical queries, machine learning features, and comprehensive aggregations.
Built directly into Tiger Postgres, Tiger Lake is designed for real-time and agentic workloads. Enhanced by TimescaleDB, Tiger Postgres adeptly manages high-ingest, time-series data, offering support for swift rollups and concurrent analytical queries at scale. This robust foundation ensures that Tiger Lake is production-ready, instilling confidence in its ability to perform seamlessly under real-world conditions.
A Real-Time System That Works Both Ways
The architecture enables continuous replication of Postgres tables into the lakehouse without the need for manual ETL processes, Kafka streaming, or custom connectors. Notably, Tiger Lake also allows for the synchronization of processed results from the lakehouse back into Postgres, empowering users to query enriched features, semantic rollups, or machine learning outputs directly from the operational database.
Kevin Otten, Director of Technical Architecture at Speedcast, shared his experience: “We stitched together Kafka, Flink, and custom code to stream data from Postgres to Iceberg—it worked, but it was fragile and high-maintenance. 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.” Early adopters like Speedcast and Monte Carlo are already harnessing Tiger Lake to streamline previously convoluted data stacks, demonstrating its capability to support production-grade, real-time intelligence without compromise.
Open Standards, Not Lock-In
Tiger Lake embodies a broader industry trend towards composable, open data systems. Unlike platforms that confine users to tightly integrated, all-in-one stacks, Tiger Lake is constructed on open formats, such as Apache Iceberg, and integrates seamlessly with standard cloud infrastructures. This design grants engineering teams the flexibility to adopt and evolve their architecture without being ensnared in proprietary control planes.
Public Beta and What’s Next
Tiger Lake is currently available in public beta through Tiger Cloud. The initial release permits users to stream Postgres tables and TimescaleDB hypertables into Iceberg-backed S3 storage, with the capability to pull data back into Postgres. Future updates are set to enhance support, including direct querying of Iceberg catalogs from within Postgres and comprehensive round-trip sync workflows that return computed insights into the operational layer.
A New Default for Intelligent Applications?
This marks merely the beginning of a roadmap aimed at minimizing the friction between live context and analytical depth. By equipping developers with a unified foundation for delivering application data and insights, Tiger Lake aspires to establish a new standard for constructing intelligent applications, free from delays, pipelines, or compromises.
