DuckDB queries Parquet, CSV, and JSON files directly with full SQL, at speeds that make a warehouse feel unnecessary for anything under a few hundred million rows. It runs embedded in Python, R, or the CLI, so there is no cluster to provision and no credentials to manage. For analysts who just want to answer a question about a file, it removes an entire category of setup.
Who it is for
A data analyst who wants to answer a question about a 5 GB Parquet file without provisioning a warehouse.
Key features
- Queries Parquet, CSV, and JSON directly
- Runs embedded in Python, R, Java, and CLI
- Full SQL including window functions and CTEs
- Reads and writes Arrow and Pandas frames
- Single writer, no server or users
- Extensions for spatial and full-text search
Where it falls short
It is not a server - one writer at a time - so it does not replace a shared warehouse, and there is no user management or row-level security.
Pricing
Free and open source with no paid tier; there is no hosted service from the project itself, so the only cost is the machine you run it on.
Prices move; the tier structure is the part that stays true. Check current pricing on DuckDB.
Alternatives to DuckDB
Frequently asked
Is DuckDB free?
Yes. Free and open source with no paid tier; there is no hosted service from the project itself, so the only cost is the machine you run it on.
What is DuckDB best for?
A data analyst who wants to answer a question about a 5 GB Parquet file without provisioning a warehouse.
What are the best alternatives to DuckDB?
The closest substitutes we list are Looker Studio and Metabase. They sit in a similar price bracket but make a different trade-off, so read the weaknesses section of each review before switching.
Which platforms does DuckDB run on?
DuckDB is available on macOS, Windows and Linux.