TigerData, the innovative team behind TimescaleDB and Tiger Postgres, has unveiled Tiger Lake, a new architectural layer designed to transform how modern applications interact with data. By bridging the real-time performance of Postgres with the scalable depth of the open lakehouse, Tiger Lake eliminates long-standing barriers between operational databases and analytical platforms. This bold step forward enables organizations to unify transactional speed with historical insight, without the burden of fragile data pipelines or tightly coupled vendor ecosystems.
With Tiger Lake, developers no longer have to choose between agility and scale. The platform delivers continuous, bidirectional data movement across systems, unlocking a new class of applications, from intelligent agents to customer-facing analytics, capable of reacting in real-time and learning from rich historical data. The result is a composable, future-proof architecture that empowers teams to build smarter, faster, and without compromise.
Eliminating the Divide Between Operational and Analytical Systems
Tiger Lake extends the capabilities of Tiger Postgres by transforming it into the operational engine of the open lakehouse. This marks a significant step toward a modular data infrastructure, where transactional performance and deep analytics can coexist without compromising architecture.
“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.”
By connecting Postgres directly to Iceberg-backed lakehouses, Tiger Lake removes the need for deferred ETL jobs and custom integration layers. It enables continuous data movement across systems in real time, allowing operational and analytical workloads to operate in tandem.
Built for Real-Time Intelligence, Not Just Storage
Tiger Lake is available as a built-in capability of Tiger Postgres. TigerData’s PostgreSQL distribution is optimized for real-time analytics, time-series, and agentic workloads. This gives developers a unified platform that supports both high-ingest operational data and analytical querying at scale.
While other databases often require trade-offs between transaction speed and historical insights, Tiger Lake is built to serve both. Developers can replicate any Postgres table directly into the lakehouse without fragile orchestration, while Tiger Postgres remains the system of record for streaming ingestion, real-time transformations, and rollups. Analytical results, such as downsampled metrics, ML features, or historical aggregates, can then be pushed back into Postgres to be queried by applications or dashboards.
According to Freedman, the architecture represents a shift away from compromise. “Tiger Lake unifies both, natively and without compromise.”
Developer Experience Without the Glue Code
One of the key advantages of Tiger Lake is its ability to simplify previously brittle infrastructure. For engineering teams managing complex, multi-layered systems to synchronize data between Postgres and Iceberg, the shift to a native, bidirectional system promises dramatic improvements in reliability and maintainability.
“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.”
Organizations like Speedcast and others are already running Tiger Lake in production, replacing fragile pipelines with a natively integrated, real-time stack that’s built to scale.
The architecture enables low-latency access to both live operational events and historical insights, bridging the gap between application context and deep analytical history. Whether powering real-time dashboards or agentic behavior, Tiger Lake aims to make intelligence directly available at the point of interaction.
Open by Default, Composable by Design
Tiger Lake was built with openness and composability at its core. Rather than promoting an all-in-one proprietary stack, Tiger Lake connects Postgres to Iceberg using open formats, avoiding the pitfalls of vendor-controlled control planes and metadata layers.
The system is designed to work seamlessly with broader cloud infrastructure, enabling integration with query engines, ML workflows, and observability platforms without lock-in. This architecture gives developers the freedom to build with the tools they trust, from S3 to Snowflake, without abandoning modularity.
Now Available in Public Beta
Tiger Lake is now in public beta, fully managed via Tiger Cloud. The initial release focuses on streaming Postgres and TimescaleDB hypertables into AWS S3 Tables in Iceberg format, as well as syncing data from S3 back into Postgres.
TigerData’s roadmap includes future capabilities such as querying Iceberg catalogs directly from within Postgres and enabling round-trip workflows where analytical insights, like aggregates or machine learning outputs, can be seamlessly reintegrated into Postgres for real-time use.
Together, these features are positioned to offer a unified foundation for real-time applications without the need to sacrifice control, performance, or flexibility.
