Snowflake vs BigQuery vs Databricks
These three blur together in marketing and stay distinct in practice. Snowflake is the easiest pure SQL warehouse, with first-class data sharing; pick it when you want analytics with minimal ops. BigQuery is the most serverless of the three, with nothing to size and deep GCP wiring; pick it when you already live on Google Cloud and want ad-hoc SQL at scale. Databricks is the lakehouse, built on Spark, Delta, and notebooks; it is strongest when data engineering and ML sit right next to analytics. Plenty of large orgs end up running two of them.
The warehouse (Snowflake), the serverless warehouse (BigQuery), and the lakehouse (Databricks). The lines blur every year — Snowflake added Snowpark and Python, BigQuery added BigLake, Databricks added Databricks SQL — but their centre of gravity still differs: SQL ease, serverless GCP-native, and Spark/ML lakehouse respectively.
Pick each when…
You want the easiest pure SQL warehouse, multi-cloud, with great data sharing and minimal operational overhead.
You are on Google Cloud and want truly serverless, ad-hoc SQL analytics with nothing to size or tune.
Data engineering and ML live next to analytics, you want Spark and open Delta/Parquet formats, and a lakehouse over your own storage.
Side by side
| Snowflake | BigQuery | Databricks | |
|---|---|---|---|
| Core model | Cloud data warehouse; storage/compute separated into virtual warehouses. | Serverless data warehouse; no clusters to manage. | Lakehouse on object storage with Delta Lake + Spark. |
| Compute / sizing | You start/stop/size virtual warehouses; per-second billing. | Fully serverless; on-demand (per-TB scanned) or capacity slots. | You run clusters/SQL warehouses (serverless options exist); DBU billing. |
| Cloud | Multi-cloud (AWS, Azure, GCP). | GCP only. | Multi-cloud (AWS, Azure, GCP). |
| SQL vs ML/eng | SQL-first; Snowpark adds Python/ML. | SQL-first; BigQuery ML for in-warehouse models. | Strongest for data engineering + ML; SQL via Databricks SQL. |
| Storage format | Proprietary micro-partitions (Iceberg tables supported). | Proprietary columnar (BigLake/Iceberg for open tables). | Open Delta Lake / Parquet on your own object store. |
| Data sharing | Best-in-class: Secure Data Sharing + Marketplace. | Strong via Analytics Hub and authorized views. | Delta Sharing (open protocol). |
| Openness / lock-in | Managed, proprietary engine; portable SQL. | GCP-coupled; serverless convenience for coupling. | Most open: data stays in open formats you control. |
Also worth a look
- ClickHouse — You want fast, self-hostable columnar analytics
- DuckDB — Single-machine or embedded analytics
- Redshift — You are AWS-native and want a managed warehouse