121–130 of 144 posts
Super excited about Quack (partially due to the name). I use duckdb for both analytics and runtime, but I do have to serve/handle/manage a giant, multi-GiB duckdb file as effectively a runtime artifact[1]. I'm aware that this isn't the _perfect_ database for this, but the mix of it being fast, having spatial support, sane coding interfaces, great dbt integration, and me being able to do everything between "run a gian…
Super excited about Quack (partially due to the name). I use duckdb for both analytics and runtime, but I do have to serve/handle/manage a giant, multi-GiB duckdb file as effectively a runtime artifact[1]. I'm aware that this isn't the _perfect_ database for this, but the mix of it being fast, having spatial support, sane coding interfaces, great dbt integration, and me being able to do everything between "run a gian…
I built a platform for some midsize companies in a specific vertical that is basically a data warehouse with some LLM-driven dashboarding and query tools on top. Typical data size 5-150gb. So I built a service layer around duckdb, where each tenant gets their own duckdb. I'm also in the boat of knowing that duckdb is not the perfect solution for this (the classic use case is running it against local data on a laptop)…
Then more recently I was given a somewhat random task to organise a motley collection of web scrapes, historic data, realtime data, data to be fetched on demand dispersed in semi-random collections. DuckDB as backing store + Claude Code that I discovered in Nov-2025 (with suitable skills and schemas) = a data agent where I could tell CC *in English* complicated data requests!? And CC would write glue shell and python code, write SQL and run it against DuckDB that housed most of the data, fetch new data, munge join filter, and present it to the user as "your data is in data slash blah". It seemed a miracle unfolded in front of my eyes! So yeah - fond of DuckDB. :-)
Latter I read this https://openai.com/index/inside-our-in-house-data-agent/ and thought "but of course".
DuckDB is one of the things I've been most excited about in a long time. Introduced it to projects at 3 companies since 2023, greatly lowering resource requirements and running it in a variety of environments. Just having the ability to do out of core bigger than memory data processing on lower end consumer grade hardware is remarkable. Thanks to the team for everything!
Curious to learn more about how people are using it? Are they downloading parquet files and running analyses locally, or are they connecting to Iceberg-like data lake and leveraging DuckDBs query engine capabilities or have you exposed an interface (REST, UI) to query your data?
In the future, I also plan to use it for testing production data pipelines: imagine you have a streaming cdc pipeline running in development env, and at the end you can dump every parquet into DuckDB as a verification — the end result should be the same. I could also use the same Database for testing, but I like DuckDB somehow.
With some of these changes, it appears to be encroaching on clickhouse territory. Or are they still very different products?
Definitely encroaching. Our last product (SaaS observability) uses Clickhouse. Our next product (self-hosted observability) uses DuckDB.
I'm looking for a lightweight client-server database where I can connect 3 or 4 GUI clients to a single database and concurrently edit the database. Low transaction volumes (probably a few edits per minute). Would DuckDb + Quack be suitable?
If you want to go with something less mainstream, why not trying Firebird DB, a very solid solution.
I'm looking for a lightweight client-server database where I can connect 3 or 4 GUI clients to a single database and concurrently edit the database. Low transaction volumes (probably a few edits per minute). Would DuckDb + Quack be suitable?
Why not MySQL/MariaDB? DuckDB and Quack might be new and flashy, but sooner or later you will need things like connection pooling, data retention, backups, replication. In a real setup there is nothing lightweight about DuckDB as it will have to answer the same issues other DB-s do. If you want to go with something less mainstream, why not trying Firebird DB, a very solid solution.
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Curious to learn more about how people are using it? Are they downloading parquet files and running analyses locally, or are they connecting to Iceberg-like data lake and leveraging DuckDBs query engine capabilities or have you exposed an interface (REST, UI) to query your data?
We use WASM DuckDB as the target for an in-browser agentic feature. Generated SQL runs against the user's individual tables that then feed in-browser dashboards. Excellent performance.
Investigating a similar use case and trying to figure out what our infra could look like!
We have bet early on DuckDB and Ducklake for Windmill and couldn't be happier. The focus on server/client mode is interesting, it opens the way for orchestrators like ours to have "lite" workers/jobs for duckdb that connect to one central bigger beefy nodes and improve the overall efficieny. I'm very curious if benchmark shows that there are performance benefits to do so thanks to co-location and overall less cpu cyc…
Earlier quoted context omitted.
If you merge PRs that have commit mesages like this, it's easy to arrive at 10000 commits in 6 months: rename to NodePointer instead format Revert "format" Revert "rename to NodePointer instead" rename to OptionalNodePtr woops update comment slot renames more renames Source: https://github.com/duckdb/duckdb/pull/23605 If every Ctrl+S is a commit, it'll go up fast. "woops"!
They seem to squash PRs using the pr description as commit message. So this sounds like a good approach to me.