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It would be great if you could Just Use Postgres for Everything (in fact I've written about this before). But in reality, your data’s often split: 🗃️ Your operational Postgres handles ingestion + app serving 🏔️ Your lakehouse lets you bring your own compute to give your data teams deeper analytics + ML on S3 🪢 And you’re stuck gluing it all together with Kafka, Flink, Airflow, reverse ETL... We didn’t love that last step, so we built Tiger Lake. It's promises a continuous, bidirectional sync between Postgres and the datalake. No pipelines. No orchestration. No lock-in. Flexible enough to ingest from any direction, power any use-case. Just open standards: Postgres, Iceberg, S3 Tables. All built into Tiger Cloud. In public today, with real companies already building on it: 🛰️ “It’s the architecture we wish we had from day one.” — Speedcast 🔬 “Lets us grow fast with infra for analytics and agentic AI.” — Lumia Health 🏭 “We finally unified real-time and historical data.” — Pfeifer & Langen 👉 https://lnkd.in/gzBdrH3K 👉 https://lnkd.in/g_ucwhXy

Tiger Lake streams them together, bidirectionally, in SQL.

Tiger Lake delivers them continuously. No fetch jobs, no delay.

Tiger Lake keeps Postgres synced with Iceberg, so your metrics stay fresh and complete.

LLMs crave context like GPUs crave power. Today's LLMs are magical things. And they live on context. The more context, the better. Here's why: Large language models are trained up to a certain date, known as the cut-off date, meaning their training data doesn’t include information after a certain point in time. Even the most recently released models like Claude Sonnet 4 have a cut-off date of March 2025. Now what if you want to ask an LLM today’s weather? Ask about how your company won the most recent deal (where the data is in Slack and Salesforce, not in the public)? Why did Timescale change their name to TigerData? You now need external data. The LLM needs more context. If you are building an AI application, you need a place to store all of the relevant context to enable the LLM to generate the optimal text. As a developer, you have two choices, neither of which is great: - Database: Fast, but expensive - Data Lake: Cheap, but slow Enter Tiger Lake: A Context Lake. Fast like a database, scalable like a lakehouse. Tiger Lake creates a real-time data loop between Postgres and Iceberg: - Apache Iceberg tables stored in Amazon S3 Tables for lakehouse integration - Continuous replication from Postgres tables or hypertables into Iceberg - Streaming ingestion back into Postgres for low-latency serving and operations - Pushing down queries from Postgres to Iceberg for efficient rollups - One system for now + then And Tiger customers are already using it: "It’s the architecture we wish we had from day one." - Speedcast "With Tiger Lake, we finally unified our real-time and historical data" - Pfeifer & Langen "A foundation for agentic AI." - Lumia Health If you’re building agents or real-time analytics, your LLM is hungry. Feed it context. Try Tiger Lake today on Tiger Cloud, and check out the Tiger Lake docs to get started.