By: Jake Smiths

In the evolving world of modern data architecture, the longstanding wall between transactional speed and analytical scale has limited innovation for years. On one side sits the operational database, powering fast user-facing applications. On the other hand, the analytical lakehouse is built for deep insights and machine learning. Bridging them has often required brittle ETL pipelines, custom code, and difficult trade-offs.

Now, TigerData, the company behind TimescaleDB and Tiger Postgres, believes it has introduced a crucial layer that could better connect the two: Tiger Lake.

A Native, Bidirectional Bridge Between Postgres and the Lakehouse

Announced today, Tiger Lake is a new architecture designed to facilitate real-time, bidirectional data movement between Postgres and Apache Iceberg-backed lakehouses, starting with native support for AWS S3 Tables. This means developers can now continuously stream operational data into the lakehouse and push back computed results, such as ML features or historical aggregates, into Postgres without needing to rely on streaming middleware or scheduled ETL jobs.

“Postgres has become the operational heart of modern applications, but until now, it’s largely been separated 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 think the industry has been looking for.”

One Architecture for Real-Time and Historical Workloads

Rather than bolting a new feature onto an existing system, Tiger Lake is fully integrated into Tiger Postgres, the company’s enhanced PostgreSQL engine optimized for real-time analytics, time-series, and agentic workloads. Built on TimescaleDB, it supports high ingest rates, live transformations, and concurrent analytical queries at scale.

The result is a system where operational and analytical workloads can coexist without the need for data duplication or synchronization tools. Real-time events stream into the lakehouse, and Iceberg-computed insights can be returned to Postgres, ready to power apps, dashboards, or even autonomous agents.

According to Freedman, this architectural alignment marks a shift from compromise to cohesion. “Tiger Lake unifies both, natively and without compromise,” he said.

From Fragile Stacks to Native Infrastructure

In today’s data engineering landscape, it’s not uncommon for teams to juggle Kafka, Flink, and custom orchestration logic just to move data between systems. But for companies like Speedcast, those challenges may soon be reduced.

“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 wished we had from day one.”

Speedcast is one of several early adopters turning to Tiger Lake as a way to reduce operational complexity while gaining real-time access to analytical insights.

Built on Open Standards, Not Proprietary Lock-In

Tiger Lake’s approach stands in contrast to vertically integrated “end-to-end” platforms. Rather than limiting users to a single stack, Tiger Lake embraces open standards: Apache Iceberg for the table format and AWS S3 as the underlying storage layer.

This allows engineering teams the flexibility to integrate with tools they already use (such as Spark, Snowflake, machine learning pipelines, or observability stacks) without being tied to a proprietary control plane.

“Tiger Lake keeps Postgres and Iceberg open, composable, and future-ready,” said the company’s announcement.

A Vision for Real-Time Intelligence

Tiger Lake is now available in public beta, fully managed on Tiger Cloud. The initial rollout supports:

• Streaming Postgres tables and TimescaleDB hypertables into AWS S3 in Iceberg format.

• Syncing Iceberg-backed data back into Postgres.

Upcoming features will enable users to query Iceberg catalogs directly from within Postgres and establish full round-trip workflows, allowing application layers to consume real-time updates enriched by historical intelligence.

The company believes Tiger Lake could offer a new model for building intelligent, responsive applications.

“Whether it’s agents, copilots, or dashboards, modern apps need real-time access to both operational events and historical context,” said Freedman. “Tiger Lake could be the way we make that possible.”

What It Means for the Industry

With Tiger Lake, TigerData isn’t just releasing another integration; it’s suggesting a new architectural model. By doing so, it challenges conventional ideas about how databases and lakehouses should interact and opens a potential path for composable, real-time data infrastructure that aligns with the needs of today’s AI-driven applications.

Whether this path becomes the new norm will depend on how quickly and widely the developer community adapts to the shift. But for now, TigerData has laid down an intriguing marker in the industry.