From Lakehouse discovery to a trained XGBoost model and a live BI dashboard in 4 minutes. Watch Gabe Weiss’s #GeminiAtWork# data demo: • Live PySpark + zero-copy joins on Lightning Engine • Explainable XGBoost on Managed Spark (90% accuracy) • Zero-token BI refreshes via Knowledge Catalog

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 →

Our borderless Lakehouse lets Gemini query AWS and Azure with no variable egress fees, read directly from Salesforce Data 360, SAP, ServiceNow, and Workday without copying data, and federate open Apache Iceberg tables across Databricks Unity, Snowflake Horizon, and AWS Glue →

Last night at Open Lakehouse + AI in Paris 🇫🇷 Aravind Segu and Edwin He presented Omnigent: A Meta Harness for AI Agents covering why adopting agents is easy and operating them isn’t, then a live demo. Thanks to everyone who came out! 🗓️ Upcoming Omnigent events:  #OpenLakehouse# #Omnigent#

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.

Just listed: one open-format lakehouse, move-in ready for your agents. Open formats, Parquet on object storage, ACID transactions, snapshots you can revisit, and a schema that evolves with you. Storage and compute are billed separately. Utilities not included. The open house is Nov. 4 at Data Outpost, where @__AlexMonahan__ (MotherDuck) and Matt Martin (State Farm) are running "Lakehouses for Robots: Scaling DuckLake and Iceberg for Agent Workloads." You'll query Iceberg and DuckLake from @DuckDB and MotherDuck with the agent of your choice, and learn to get the most out of your local laptop so you can build faster. Performance tuning and agent compartmentalization keep your agents' answers quick and safe. Whether you're new to lakehouses or a seasoned expert, you'll leave with a working lakehouse example. Bring a laptop. 🎟️ Tickets: