When #DuckDB-Wasm# was launched in 2021, databases could not be persisted: everything lived in the Wasm heap and vanished when the tab closed. Keeping data meant serializing tables to Parquet, storing the bytes in IndexedDB, and re-registering them on the next page load. This was doable, but had to be handled at the application layer and was not offered out of the box by DuckDB-#Wasm#. Modern browsers now ship the Origin Private File System (OPFS), a per-origin, sandboxed file system with random-access reads and writes. DuckDB-Wasm can use it as a storage backend. This post by @carlo_piovesan and @GeertjanW shows you how:

Are you attending #dbtSummit# in Las Vegas this week? 😎 Don’t miss Hannes Mühleisen deliver his talk “dbt Without the Warehouse (or the Bill): DuckDB End to End” Hannes’s talk starts at 10:00 a.m. PT on Friday, Sept 18th – on Level 3, Gracia 4. Don’t miss it! Full abstract and info here: And say hi to Hannes if you stop by! 🦆

New third-party benchmark results for NVIDIA Vera CPU are in! @duckdb put the Vera CPU to the test and measured 1.5x higher TPC-H performance compared to a leading x86 CPU. Check out the results ⤵️

@duckdb I’ve watched Claude write a whole Python script just to peek at a CSV. Slow, messy, and wrong more than once. This plugin actually gets how I work. Open the file. Ask the question. Get the answer. Installing this tonight.

More likely than not, you’ve been using AI tools such as Claude Code for day-to-day work. You may have noticed that when AI needs to look at a data file, it makes use of Python, writes a small script, runs it, and then reads the output. It’s an approach that works most of the time, but it’s slow and not always accurate. The duckdb-skills plugin gives Claude Code a growing number of skills that use the DuckDB CLI to read data files, run queries, convert formats, explore object storage, work with spatial data, search the documentation and recall earlier sessions. Here’s how you can get started with the new plugin:

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:

🦆🤓 When Denis Hirn implemented DuckDB’s first recursive CTE operator in 2020, correctness determined the design: evaluate the non-recursive term once, then evaluate the recursive term until the next working table is empty. That established the right semantic contract, but reusable runtime state was scoped too narrowly. In the upcoming DuckDB v2.0 release, we assign those scopes explicitly. #DuckDB’s# recursive CTE engine now treats recursion as one long-lived computation: it retains eligible epoch-invariant state, chooses execution modes from exact frontier cardinalities and physical work, probes keyed state directly and gives `USING KEY ... UNION` changed-key semantics. Read more in this preview of the forthcoming v2.0 release:

Coming to Boston for the @VLDBconf? Or maybe you’re in town just because Boston is really awesome? Maybe, even, you live and work in the Boston area? (Ducks aren’t fortune tellers, sorry. 🔮) No matter your reason, we hope you’ll join us for the DuckDB #Boston# meetup on September 3rd starting at 5:30 p.m. happening at W Boston hotel (100 Stuart St). Come meet some fellow #DuckDB# devs, learn from the technical talks, and enjoy some drinks and snacks and quacks! 🦆 🦆 🦆 Admission is free, but registration is required. Full agenda here: Who’s coming?

What’s Next for DuckDB? 🦆🤔 Hannes Mühleisen, co-creator of DuckDB, was recently interviewed by Kris Jenkins of the Developer Voices podcast on every challenge, reward, twist, and turn of building a database as popular as DuckDB. From the early days of the product to the most recent acquisition of @ducklabs_com by @awscloud, Hannes – and what it means for the future of the open source DuckDB, DuckLake, and Quack projects*. It’s an interview as much as a story – an odyssey of what it means to build not only an open source product, but also a community, a company, and what success looks like in the day to day journey along the way. 📺 Watch the video episode (or read the transcript) here: 🎧 Or, listen wherever you get your podcasts. *Spoilers: DuckDB, DuckLake, Quack, and other open source projects in the DuckStack will remain open source and the project’s intellectual property, MIT license, and trademark are controlled by the DuckDB Foundation. The DuckLabs company is joining AWS, but the OSS projects remain OSS and controlled solely by the Foundation. 🦆 🦆

Today, @ducklabs_com is joining @awscloud. The move is expected to be completed by early September. Joining AWS gives DuckLabs the resources and reach to bring DuckDB, DuckLake, and the Quack protocol to many more developers and organizations – and to pursue ideas at a scale that would have been difficult for us to reach alone. The DuckLabs team will remain together in Amsterdam and will continue working for the Duck Stack community as an AWS subsidiary. Most importantly, #DuckDB# and the other open source components of the Duck Stack will remain free and open source under the MIT license, with the non-profit DuckDB Foundation continuing its stewardship of the projects. This is a significant moment for DuckLabs and the Duck Stack community. It marks the end of one chapter that we are immensely proud of, and the beginning of another that we believe will take DuckDB much further. Read the full announcement here: #AWS#

New blog post: DuckDB Table Functions in Java 🦆☕ In a large organization, data is spread across many systems, including relational databases, document stores, message queues, data lakes, and cloud data warehouses. The layer that ties these together is often a JVM-based distributed query engine. Teams increasingly add #DuckDB# to these Java environments for fast single-node analytics. The DuckDB #Java# client can register table functions written in pure Java, exposing any Java-accessible data source as a SQL table. That turns @duckdb into a single-node query engine for heterogeneous joins across remote systems and local files, with no export step. Dive into the rest:

Quack, quack: Here’s your chance to meet fellow DuckDB devs in person! 🦆 Join us for the @DuckDB Boston meetup on September 3rd starting at 5:30 p.m. Enjoy some drinks and snacks, meet other DuckDB users, and learn from the technical talks (just announced) including: • @szarnyasg from @ducklabs_com 👨🏻‍🔬 • @gotdan from @BostonChildrens on unlocking healthcare data with DuckDB and open standards 🏥 • Richard Wesley from DuckLabs on windowing improvements in #DuckDB# v2.0 🪟 (venue still TBD, btw) The meetup takes place concurrently with the @VLDBconf, so it’s the perfect chance to meet fellow database researchers and practitioners from the Boston area and from all around the world. 🌏🌍🌎 Admission is free, but registration is required: Will we see you there? 🦆🦆

` Chunked Query Results in the DuckDB Java Driver ` 🦆☕ The DuckDB #Java# driver can now return query results as a lazily fetched sequence of columnar data chunks, avoiding JDBC’s one-row-at-a-time ResultSet and its per-value overhead. DuckDB is a columnar, vectorized database. Every operator inside the engine works on data chunks: batches of column vectors, 2,048 rows at a time. That is a big part of why #DuckDB# is fast: the engine amortizes interpretation overhead over thousands of values instead of paying it once per value. JDBC, on the other hand, was designed in 1997. Here’s how the new DuckDB Java driver bridges the two: