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Dev tools · March 20, 2025

GR

GizmoSQL – Run DuckDB as a Server with Arrow Flight SQL

Hi, I'm Philip Moore - the founder of GizmoData, and creator of GizmoSQL - an Apache Arrow Flight SQL Server - with DuckDB (or SQLite) back-end execution engines. GizmoSQL is a composable SQL server with Arrow Flight SQL, DuckDB, and SQLite - with the intention of making it easy to run DuckDB (or SQLite) as a server - usable by multiple people from a client (remote) computer. It also adds security (authentication) and encryption of traffic with TLS. To run GizmoSQL - see the steps in the README.md - where you can see how easy it is to run the server as well as how to connect via ADBC and…

In plain words

GizmoSQL is an Apache Arrow Flight SQL server that runs DuckDB or SQLite as a backend, enabling multiple remote users to query these embedded databases from client computers. It adds authentication and TLS encryption for secure data transmission. The tool is designed for developers and data teams who need to share DuckDB or SQLite databases across a network, with support for connections via ADBC and JDBC clients like DBeaver and Python. GizmoSQL is available via Docker, Linux, and macOS.

written from the facts on this page · September 2026

From the sources

In the maker’s words, at launch

Hi, I'm Philip Moore - the founder of GizmoData, and creator of GizmoSQL - an Apache Arrow Flight SQL Server - with DuckDB (or SQLite) back-end execution engines. GizmoSQL is a composable SQL server with Arrow Flight SQL, DuckDB, and SQLite - with the intention of making it easy to run DuckDB (or SQLite) as a server - usable by multiple people from a client (remote) computer. It also adds security (authentication) and encryption of traffic with TLS. To run GizmoSQL - see the steps in the README.md - where you can see how easy it is to run the server as well as how to connect via ADBC and JDBC from a remote client - such as DBeaver, Python, etc. The easiest way to run GizmoSQL is via Docker - but there are downloads for Linux and macOS for both x86-64 and arm64 platforms (download links in the README). Why?: As you may know, DuckDB and SQLite are embedded systems - they don't enable client connectivity, and they aren't really designed for concurrency. I've built GizmoSQL to work around that - because I believe the DuckDB engine is very powerful, and I feel like a lot of customers overpay and run distributed compute (i.e. Spark) when they don't really need to. Making it easy to have remote connectivity to DuckDB can make it easier to migrate SQL workloads from Spark or other expensive commercial platforms to this engine - with a much simpler architecture/infrastructure. It is my intention to make GizmoSQL a commercial product - licensed for production use by organizations, but free for developers to code with - evaluate, and test. A little bit of backstory: * I built the initial version of this while working for a former employer - it wasn't their core focus, so they open-sourced that early version. After I left there, I forked the product and have improved it substantially - to support concurrency of both reads and writes, improving security, as well as keeping it up to date with the latest versions of Apache Arrow and DuckDB. * This project evolved from a prototype created by the brilliant Tom Drabas. * It feels a little weird trying to make a commercial product based upon DuckDB, but MotherDuck started it :P - and I've contributed (albeit very little) to the DuckDB and Apache Arrow projects in the form of a couple of PRs. I'm really excited about this project - I have run benchmarks of this product against commercial platforms such as Snowflake and Databricks SQL - and it holds its own running the 22-query TPC-H SF1TB benchmark, especially on cost. See the graph at: https://gizmodata.com/gizmosql Getting started: Github README: https://github.com/gizmodata/gizmosql-public/blob/main/READM... DockerHub: https://hub.docker.com/r/gizmodata/gizmosql GizmoSQL homepage: https://gizmodata.com/gizmosql Phil's Github profile: https://github.com/prmoore77 Thanks for your time and feedback in advance.

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