DuckDB vs Trino
Both are alternatives to Snowflake. Here's how they stack up — verified facts, no spin.
Also searched as Trino vs DuckDB — same comparison, one verdict.
DuckDB
The warehouse you did not need — analytics in a single file, on your laptop.
DuckDB is an in-process analytical database: no server, no cluster, no account. It queries Parquet and CSV directly, runs inside Python or R, and handles hundreds of gigabytes on a normal machine. MIT, around 40k stars. Its real contribution is the uncomfortable question it poses to this whole category — a very large number of Snowflake deployments hold data that DuckDB would answer instantly, for nothing, without anyone provisioning a warehouse. Ask how much data you actually have before you shop for infrastructure to hold it.
Trino
Query everything where it already lives, without moving any of it.
Trino is a distributed SQL engine that queries data in place — S3, Postgres, MySQL, Kafka, Elasticsearch — and joins across all of them in a single statement. Apache-2.0, around 13k stars, built by the team behind Presto. The point is that it removes the loading step entirely: no ingestion pipeline, no second copy, no drift between the warehouse and the source. That is a genuinely different architecture from Snowflake rather than a cheaper version of it, and it suits organisations whose data is scattered and will stay scattered.
Side by side
| DuckDB | Trino | |
|---|---|---|
| Sovereignty Score | 97 | 91 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | Apache-2.0 |
| Pricing | Free and open source. No server, so no hosting cost either. | Free and open source. Managed options from Starburst and others. |
DuckDB edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
DuckDB
Strengths
- +No infrastructure at all — it is a library, not a service
- +Queries Parquet and CSV in place with no loading step
- +Handles hundreds of gigabytes on ordinary hardware
- +MIT, tiny, and embeds anywhere
Trade-offs
- −Single-machine — there is no cluster and no horizontal scale
- −Not built for many concurrent users
- −No built-in governance, access control or sharing
- −Wrong tool once you genuinely have warehouse-scale data
Trino
Strengths
- +Queries data where it lives — no ingestion, no second copy
- +Joins across completely different systems in one SQL statement
- +Apache-2.0 with a large connector ecosystem
- +Proven at very large scale
Trade-offs
- −A cluster to operate, with real memory tuning
- −Only as fast as the slowest underlying source
- −No storage of its own — it is an engine, not a warehouse
- −Java operations knowledge assumed
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Facts verified 2026-08-03. Licenses and pricing change — spotted something out of date? That's a correction we want.