Apache Doris vs Google BigQuery
Both are alternatives to Snowflake. Here's how they stack up — verified facts, no spin.
Also searched as Google BigQuery vs Apache Doris — same comparison, one verdict.
Apache Doris
Real-time analytics with the operational simplicity Snowflake users expect.
Doris aims at the gap between ClickHouse's speed and a warehouse's ease: MySQL-compatible protocol, so existing clients and BI tools connect unchanged, real-time ingestion, materialised views maintained automatically, and considerably less tuning than ClickHouse demands. Apache Software Foundation governance, Apache-2.0, around 16k stars. It is the option to look at when the team wants ClickHouse economics but nobody wants to become a ClickHouse specialist.
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
| Apache Doris | Google BigQuery | |
|---|---|---|
| Sovereignty Score | 92 | 32 |
| Open source | Yes | No |
| Self-hostable | Yes | No |
| Local-first | Yes | No |
| License | Apache-2.0 | Proprietary (hosted service) |
| Pricing | Free and open source. Managed offerings available from vendors. | About $6.25 per TB scanned on demand, or flat-rate capacity. Storage around $20/TB/month. Checked 2026-08-03. |
Apache Doris edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Apache Doris
Strengths
- +MySQL protocol — existing BI tools and clients just connect
- +Materialised views maintained automatically, no manual refresh
- +Apache Software Foundation governance
- +Markedly less tuning required than ClickHouse
Trade-offs
- −Smaller community outside China than ClickHouse
- −Documentation in English is thinner
- −Fewer integrations in the Western data stack
- −Still a cluster to run
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.