The Good Tech Companies - Tiger Lake Launches to Unify Postgres and Lakehouse for Real-Time Analytics and AI

Episode Date: September 4, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/tiger-lake-launches-to-unify-postgres-and-lakehouse-for-real-time-analytics-and-ai. Tiger Lake unifies Postgres and the lakehouse with a real-time data loop, simplifying pipelines and powering dashboards, monitoring, and AI-driven agents. Check more stories related to cloud at: https://hackernoon.com/c/cloud. You can also check exclusive content about #tiger-lake-data-architecture, #real-time-postgres-analytics, #unified-data-pipelines, #postgres-iceberg-integration, #tiger-cloud-public-beta, #operational-medallion-model, #agentic-postgres-ai, #good-company, and more. This story was written by: @tigerdata. Learn more about this writer by checking @tigerdata's about page, and for more stories, please visit hackernoon.com. Tiger Lake, now in public beta, bridges Postgres and the lakehouse with a continuous, real-time data loop. It removes the need for pipelines and orchestration, enabling unified dashboards, faster monitoring, and AI agents grounded in live + historical context. Built into Tiger Cloud, Tiger Lake simplifies architectures while powering scalable, intelligent, real-time applications.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Tiger Lake launches to Unify Postgres in Lakehouse for real-time analytics on die by Tiger data, creators of timescale DB. Modern applications are becoming more dynamic, more intelligent, and more real-time. Dashboards refresh with incoming telemetry. Monitoring systems respond to shifting baselines. Agents make decisions in context, not in isolation. Each depends on the same foundational requirement, the ability to unify live events with deep historical state. Yet the data remains fragmented. Operational systems build on postgres, handle ingestion and serving. Analytical systems built on the lakehouse, handle enrichment and modeling. Connecting the means stitching together

streams, pipelines, and custom jobs, each introducing latency, fragility, and cost. The result is a patchwork of systems that struggle to deliver the full picture, let alone do so in real time. This fragmentation doesn't just slow teams down. It limits what developers can build. You can't deliver real-time dashboards with historical depth or ground agents in fresh operational context when the data is split by design. This architectural divide is no longer sustainable. Tiger Lake bridges that divide. Now in public beta, it introduces a new data loop, continuous, bidirectional, and deeply integrated, between post grass and the lakehouse. It simplifies the stack, preserves open formats and brings operational and analytical context into the same system.

Introducing Tiger Lake. Real-time data, full context systems. Tiger Lake eliminates the need for external pipelines, complex orchestration frameworks, and proprietary middleware. It is built directly into Tiger Cloud and integrated with Tiger Postgres, our production grade Postgres engine for transactional, analytical, and agentic workloads. The architecture uses open standards from end to end, Apache iceberg tables stored in Amazon S3 tables for lakehouse integration. Continuous replication from Postgres tables are hyperdibles into iceberg. Streaming ingestion back into Postgres for low latency serving and operations. Pushing down queries from Postgres to iceberg for efficient roll-ups. These capabilities come built in. What previously

required flint jobs, DAG schedulers, and custom glue now works natively. Streaming behavior and Shemacompatibility are designed into the system from the start. To understand how Tiger Lake reshapes data architecture, it helps to revisit the medallion model and consider how it evolves when real-time context becomes a core design principle. You can think of it as an operational medallion architecture. Bronze, raw data lands in iceberg backed S3. Silver, cleaned and validated data is replicated to Postgres. Gold. Aggregates are computed in Postgres for real-time serving, then streamed back to Icebert for feature analysis. Traditional bronze silver gold workflows were built for batch systems.

Tiger Lake enables a continuous flow where enrichment and serving happen in real time. This shift transforms an overly complex pipeline into a dynamic and simpler real-time data loop. Context and data moves freely between systems. Operational and analytical layers stay connected without redundant jobs or duplicated infrastructure. All data remains native, up-to-date, and query. with standard SQL. Tiger Lake supports a single right path that powers real-time applications, dashboards, and THE Lakehouse, using the architecture that best fits the developer. Users can write data to Postgres, then have appropriate data and roll-ups automatically sync to their

lakehouse. Conversely, users already feeding raw data into the lakehouse can automatically bring it to Postgres for operational serving. Now, applications can reason across the now and the then, without orchestration code or synchronization overhead. Greater than, we stitched together Kafka, Flink, and custom code to stream data from greater than Postgres to iceberg. It worked, but it was fragile and high maintenance, said greater than Kevin Auden, Director of Technical Architecture at Speedcast. Tiger Lake greater than replaces all of that with native infrastructure. It's the architecture we wish greater than we had from day one. From architecture to outcomes, Tiger Lake enables real-time systems

that were previously too complex to operate or too expensive to build. Customer-facing dashboards dashboards can now combine live metrics with historical aggregates in a single query. There is no need for dual stacks or stale insights. Tiger Lake supports high throughput ingestion at production scale, powering pipelines that visualize billions of rows in real time. Everything lives in one system, continuously updated and instantly queryable. Greater than, with Tiger Lake, we finally unified our real-time and historical data, said greater than Maxwell Carrot, led IoT engineer at Pfeiffer and Langen. Now we seamlessly greater than stream from Tiger Postgres into iceberg, giving our analysts

the power to greater than explore, model, and act on data across S3, Athena, and Tiger data. Monitoring systems with a single source of truth and a continuous data loop, alerting becomes faster and more reliable. Engineers can run one SQL query to inspect fresh telemetry and historical incidents together, improving triage speed, reducing false positives, and staying focused on what matters. Simplifying the data plane also improves system resilience. Tiger Lake lets monitoring systems operate on the same live operational backbone, where Iceberg provides historical depth and Tiger Postgres delivers low latency access. Agents Tiger Lake makes grounding possible without additional infrastructure. Developers can embed recent user activity and long-term interaction

history directly inside Postgres. There is no need for orchestration, vector drift management or custom AI pipelines. Imagine a support agent receives a new inquiry. The large body of historical support cases remain in iceberg, while Tiger Lake created automated chunk and vector embeddings in Postgres. Now vector search against the operational database can answer AI chat questions quickly, while ensuring that embeddings stay fresh and up to date without complex orchestration pipelines. In doing so, Tiger Lake is also a key building block in what we call Agentic Postgres, a Postgres foundation for intelligent systems that learn, decide, and act. Greater than, with Tiger Lake, we believe Tiger

data is setting a strong foundation for greater than turning Postgres into the operational engine of the open lakehouse for greater than applications, said Ken Yoshioka, CTO, Lumia Health. It allows us the greater than flexibility to grow our biotech startup quickly with infrastructure designed greater than for both analytics and agentic AI. Companies like Speedcast, Lumia Health, and Fyfer and Langen are already building full context and real-time analytical systems with Tiger Lake. The Sharkitecture's power industrial telemetry, agentic workflows, and real-time operations, all from a unified, continuously streaming platform. Available in public beta on Tiger Cloud, Tiger Lake is available now in public beta on Tiger Cloud, our managed platform for real-time applications and analytical

systems. It supports continuous streaming from Tiger Postgres to iceberg-backed Amazon S3 tables using open formats. Coming soon. Round-trip intelligence later this summer, query iceberg catalogs directly from within Postgres. Explore, join, and reason across lakehouse and operational data using SQL. Fall 2025. Full round-trip workflows. Ingest into Postgres, enrich an iceberg and stream results back automatically. This lets us. developers move from event to analysis to action in one architecture. How to set up Tiger Lake getting started is simple. No complex orchestration or manual integrations. Create a bucket for iceberg compatible S3 tables. Provide ARN permissions to Tiger Cloud. Enable table sync in

Tiger Postgres. The future of data architecture is real-time, contextual, and open. Tiger Lake introduces a new kind of architecture. It is continuous by design, scalable by default, and optimized for applications that need full context and complete data in real time. Operational data flows into the lakehouse for enrichment and modeling. Enriched insights flow back into Postgres for low latency serving. Applications and agents complete the loop, responding with precision and speed. We believe this is the foundation for what comes next. Systems that unify operational use cases and internal analytics.

Architectures that reduce complexity instead of compounding it, workloads that are not just reactive but grounded in understanding. You should not have to choose between context and simplicity. You should know they've to patch together systems that were never designed to work together. And you should not have to re-platform to evolve. Together with next generation storage architecture and our Postgres native AI tooling, Tiger Lake forms the backbone of agentic Postgres. This is a foundation built for intelligent workloads that learn, simulate, and act. We'll share more soon. Try it today on Tiger cloud and check out the Tiger Lake Docks to get started. Mike thank you for listening to this Hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn and publish.