With Lakehouse federation for AlloyDB, you can query data in Iceberg and BigQuery from the PostgreSQL plane. Get historical insights in BigQuery or Iceberg without any data movement →

DuckLake: The Lakehouse That's Just SQL & Parquet [Talk Python to Me, Ep. 562] How many files does your query read before it reads any data? On some data lakes, you go through JSON and metadata files first, just to learn which Parquet files matter. #DuckLake# asks one SQL question instead. The metadata lives in a real database. The data stays in plain Parquet. That's the entire format. With Quack as the catalog, DuckLake handles 200 transactions a second under heavy contention. No other open table format comes close. In this episode of @TalkPython, host @mkennedy talks with @holanda_pe, lead developer for DuckLake at DuckLabs, and Guillermo Sanchez Dionis, who works on both DuckLake and the new Quack protocol – together they discuss all the details of DuckLake, Quack, and more. Watch the episode here or listen wherever you get your podcasts:

An AI-ready open lakehouse has three layers: Apache Iceberg for storage, catalog federation for governance, and MCP for AI agent access. More on our borderless lakehouse with @googlecloud: 🧊

Querying JSON in open lakehouse tables means parsing everything at runtime — no predicate pushdown, no skipping. Iceberg v3 Variant flips that by: ✦ Shredding nested data into typed columns at write time ✦ Enabling predicate pushdown at the column level Result: In an 11-query benchmark on identical Iceberg tables, Snowflake was 2.3x faster at equivalent compute.

That is the multi-engine lakehouse goal. Apache Iceberg gets us closer, but teams still need to design for interoperability, governance and shared business logic. Check out the guide for what's solved and what isn't:

📣 Hey Paris: Join us on Sept 23 for the next Open Lakehouse + AI meetup! Aravind Segu and Edwin He will cover why to use a meta-harness: collaborate on your work, exercise control, and choose your coding agent harnesses. The talk includes a demo of Omnigent’s workflow, including @opentelemetry traces in tools like @MLflow. 🗓️ Wed, Sept 23 | 6:00–9:30 PM GMT+2 📍 La Fondation, Paris 🎟️ Register: #Paris# #Omnigent# #OpenSource#

Modernizing recurring SQL ETL doesn’t require rewriting the workflows you already rely on. Declarative ETL is coming directly into Lakehouse, so SQL practitioners can keep working in familiar SQL workflows while using declarative patterns for common recurring tasks: ↳ APPEND for incremental ingestion ↳ AUTO CDC for change data capture ↳ REPLACE WHERE for targeted batch refreshes Databricks handles the incremental processing, update logic, scheduling, and orchestration needed to run them reliably.

Today, we announced that we crossed $7B in revenue run-rate, growing over 80% year over year in Q2. We also shared: 🚀 $100M+ revenue run-rate for Lakebase 🚀 $1.5B+ revenue run-rate for Lakehouse, growing over 100% year over year 🚀 Continued positive adjusted free cash flow And we raised $5B in our latest fundraise. We’ll use this capital to invest in: 1️⃣ Lakebase, our serverless Postgres database built for AI agents 2️⃣ Genie, our AI coworkers that actually understand your business data 3️⃣ Unity AI Gateway, our multi-AI governance solution that helps control costs @iamVictorDey shares more in @Forbes:

⚡ AI Brief ⚡ Databricks has raised $5 billion at a $190 billion valuation. The company originally sought to raise just $1 billion, but received approximately $15 billion in investor interest before ultimately accepting $5 billion, making the round roughly 3x oversubscribed. Founded in 2013, Databricks is a leading enterprise data and AI platform. Its Lakehouse architecture helps organizations unify their data and provides the foundation for AI models and AI agents. The round was led by Coatue, with participation from Blackstone, MGX, T. Rowe Price, Sixth Street, Point72, TPG, Clearlake, BOND, and Premji Invest. Existing investors including a16z, Thrive Capital, and Dragoneer also increased their commitments. Databricks has now surpassed a $7 billion annual revenue run rate, growing more than 80% year over year. The company plans to use the new capital to expand its AI agent capabilities, betting that in the AI agent era, competitive advantage will come from controlling the data agents rely on—not just building the most powerful models. Source: Databricks, TechCrunch