In the ever-evolving landscape of data infrastructure, the divide between real-time operational systems and analytical platforms has long been a source of engineering frustration. Today, TigerData (the team behind TimescaleDB and Tiger Postgres) announced Tiger Lake, a new architectural layer designed to close that gap.
Built as a native, bidirectional bridge between Postgres and Iceberg-backed lakehouses (beginning with AWS S3 Tables), Tiger Lake enables seamless data movement across systems without pipelines, vendor lock-in, or architectural compromise.
“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.”
From Glue Code to Native Infrastructure
For many organizations, keeping operational databases and analytical systems in sync has meant building and maintaining brittle, multi-layered architectures. These setups, often involving Kafka, Flink, and custom ETL scripts, are difficult to manage and scale.
Tiger Lake replaces this fragility with native, continuous data synchronization. Postgres becomes both the live system of record and the conduit to long-term, analytical context, making real-time and historical data available from a single source of truth.
Kevin Otten, Director of Technical Architecture at Speedcast, underscored the operational pain that Tiger Lake resolves:
“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.”
Why Tiger Lake Matters for Modern Applications
Tiger Lake is embedded directly into Tiger Postgres, TigerData’s PostgreSQL distribution optimized for real-time analytics, time-series data, and agentic workloads. Built on TimescaleDB, it is designed to support production-grade ingest rates, transformation logic, and concurrent querying.
With Tiger Lake, developers gain:
- Native replication of Postgres tables into Iceberg-backed lakehouses
- The ability to sync analytical results, such as ML features or aggregated metrics, back into Postgres
- A fully open and composable architecture based on Apache Iceberg and AWS S3 Tables
- Compatibility with cloud-native tools like Spark, Snowflake, and machine learning pipelines
This design allows operational and analytical workloads to work in tandem without compromising speed, scale, or system simplicity.
Open by Default, Composable by Design
Rather than introducing another vertically integrated stack, Tiger Lake embraces open standards from the ground up. It uses Apache Iceberg as its table format and Amazon S3 Tables as its storage foundation, ensuring compatibility across the cloud data ecosystem.
TigerData positions Tiger Lake as a future-proof alternative to closed systems, offering teams the flexibility to evolve their infrastructure with the tools of their choice, and not the constraints of a single vendor.
Available Now. Evolving Fast.
Tiger Lake is now available in public beta on Tiger Cloud. The initial release includes:
- Streaming of Postgres and TimescaleDB hypertables into Iceberg-formatted S3 Tables
- Syncing data from Iceberg-backed storage back into Postgres for live operational use
Future updates will unlock direct querying of Iceberg catalogs from within Postgres and enable full round-trip workflows, allowing analytical insights to be instantly usable inside applications, dashboards, or agents.
Redefining the Data Stack
Tiger Lake arrives at a moment when organizations are increasingly seeking agile, composable architectures that can support both real-time operations and analytical intelligence. By removing the overhead of data pipelines and stitching tools, TigerData is offering a new model: one where speed and depth don’t compete; they complement.
For teams building intelligent applications that must react and learn in real time, Tiger Lake may well become the default architecture they never knew they needed, until now.