We released DuckDB 2.0 on Wednesday, leading with the Python, Java and command-line clients, plus the core extensions. By Friday, the rest of the ecosystem caught up with the new version. All primary clients support DuckDB 2.0, including Go, Node.js, R and Rust. Most community extensions have also been updated and rebuilt for DuckDB 2.0, and there are even new ones out there For example, @MistralAI released their new Timestral time-series model family, and there’s already a DuckDB extension for it, contributed by Sylvain Utard! Timestral forecasts that if you’re based in Europe, you’ll have an extra hour this weekend – perfect for trying it out using our new compact syntax: INSTALL AND LOAD timestral FROM community;

DuckDB 2.0 was released yesterday with a large set of core extensions. But one of the things that make DuckDB stand out is its extensive Community Extensions ecosystem. Many of our amazing contributors have already migrated their extensions, while others are in the process of doing so. To help users monitor this transition, Community Extensions now have a compatibility matrix that shows the availability of an extension for a given platform and DuckDB version combination. See for example the DuckPGQ extension's matrix at duckdb.org/community_extensi….

🦆 Announcing DuckDB 2.0.0 LTS “Cinnamon Teal” We are proud to release DuckDB 2.0.0, named after the cinnamon teal (Spatula cyanoptera), a striking reddish-brown duck found in the western Americas. 🌎 DuckDB 2.0.0 is the biggest DuckDB feature release so far. It ships a polished version of the Quack protocol, which turns DuckDB into a full-fledged server. It improves performance and lowers costs through supporting asynchronous I/O, a first-class VARIANT type, and a dedicated “agent mode”. It also empowers extension developers with a new SQL parser that allows adding new statements, clauses, and keywords, a new API to build on, and a new mechanism to distribute extensions more easily. In our announcement blog post, we summarize the highlights and then walk through the key changes and give guidance on how to migrate to 2.0. For the complete release notes, see the release page on GitHub⁠. duckdb.org/2026/10/21/announ… #DuckDB

What’s Inside an Iceberg Table? 🧊 📦 An Apache Iceberg table looks like a regular table when you query it, but beneath the surface…well, it’s like an iceberg. 😅 Underneath, you’ll find a small tree of files: one JSON file at the top, a few layers of Avro files in the middle, and Parquet files at the bottom. When something is slow or looks wrong, it helps to know what’s in that tree. DuckDB can read all three formats, so you can inspect every layer with plain SQL. In this post, we create a small Iceberg table, change it a few times, and then walk through the files from the top down: duckdb.org/2026/10/13/inside… #ApacheIceberg #DuckDB

Agent Mode in the DuckDB CLI 🤖 🦆 The #DuckDB v2.0 CLI has an agent mode that gets AI coding agents to a correct result faster and with fewer tokens. When the CLI detects an agent, it prints compact Markdown tables instead of padded boxes, says clearly when a result was cut, stops runaway queries early, and reports errors as JSON. Long queries announce their expected cost before they start, so the agent can decide whether to wait. Learn more in today’s blog post: duckdb.org/2026/10/09/agent-…

[ICYMI:] DuckDB’s co-creator on why SQL won 🏁 In this interview with @letsdatascience , DuckDB co-creator Hannes Mühleisen argues that the data industry misdiagnosed SQL’s problems a decade ago. What developers *actually* hated in 2013 was the install, the server, and the client protocol, not the language itself. In the end, the NoSQL systems that tried to replace SQL only grew back into something SQL-shaped – minus 40 years of experience. 🫤 Hannes points to public workload data from Snowflake and Amazon showing that only a small fraction of a percent of analytical queries actually need a cluster, but that the database field spent 2 decades building for that edge case anyways. He also confirmed an unannounced DuckDB launch expected later this year. Catch the full interview here: letsdatascience.com/news/duc…

Attending the Rows & Columns Summit in San Francisco today? *THE* Hannes Mühleisen, co-creator of @duckdb, will give a talk exploring the architecture question that won't go away: OLTP and OLAP, together or apart? His talk “Nobody Knows What OLTP Is: DuckDB Moves to the Middle” is scheduled for 1:30 p.m. on Sept 22nd Venue: Contemporary Jewish Museum, San Francisco Sassy abstract and more info here: duckdb.org/events/2026/09/22…