DuckDB vs Google BigQuery
Both are alternatives to Snowflake. Here's how they stack up — verified facts, no spin.
Also searched as Google BigQuery 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.
Google BigQuery
Serverless warehousing with no cluster to size — if you are already on GCP.
BigQuery is fully serverless: there is no warehouse to start, stop or size, you write SQL and Google works out the compute. On-demand pricing is roughly $6.25 per terabyte scanned, so a well-partitioned table can be very cheap and an unpartitioned `SELECT *` can be alarming. Included here because for a team already inside Google Cloud, the honest comparison is not open versus proprietary but which proprietary — and BigQuery's operational simplicity genuinely beats Snowflake's for infrequent workloads.
Side by side
| DuckDB | Google BigQuery | |
|---|---|---|
| Sovereignty Score | 97 | 32 |
| Open source | Yes | No |
| Self-hostable | Yes | No |
| Local-first | Yes | No |
| License | MIT | Proprietary (hosted service) |
| Pricing | Free and open source. No server, so no hosting cost either. | About $6.25 per TB scanned on demand, or flat-rate capacity. Storage around $20/TB/month. Checked 2026-08-03. |
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
Google BigQuery
Strengths
- +Genuinely serverless — nothing to size, start or stop
- +Cheap for infrequent queries against well-partitioned data
- +Native integration with the Google Cloud and Workspace estate
- +Strong ML and geospatial support built in
Trade-offs
- −Proprietary, hosted, and deepens Google Cloud lock-in
- −Scan-based pricing punishes careless queries severely
- −Egress costs make leaving expensive
- −Practically requires you to be on GCP already
Related alternative guides
Facts verified 2026-08-03. Licenses and pricing change — spotted something out of date? That's a correction we want.