Comparison
Databricks vs BigQuery: lakehouse or serverless warehouse?
Databricks and Google BigQuery compared: architecture, who each suits, data engineering and machine learning, pricing models, open formats, Google Cloud integration and when to use both.
Hemant Walchale
· 2 min read
Databricks and BigQuery overlap on SQL analytics but start from different places. BigQuery is a serverless, SQL-first warehouse deeply integrated with Google Cloud. Databricks is a lakehouse built for data engineering, streaming and machine learning that also serves SQL. Which one fits depends on who does the work and what the data is used for.
The short answer
- Choose BigQuery if most users are analysts writing SQL, you are committed to Google Cloud, and you want a warehouse with essentially nothing to manage.
- Choose Databricks if data engineering in Python, streaming, machine learning or GenAI are central, you want open formats you control, or you run on several clouds.
- Use both when a Google-centric analytics team and an engineering and ML team share an estate.
How they compare
| Databricks | BigQuery | |
|---|---|---|
| Model | Lakehouse on open Delta tables in your cloud storage | Serverless warehouse with managed storage |
| Clouds | AWS, Azure, Google Cloud | Google Cloud |
| Strength | Data engineering, streaming, ML and AI | Serverless SQL analytics, Google ecosystem |
| Languages | SQL, Python, Scala, R | SQL (plus Python through notebooks and BigQuery DataFrames) |
| Pricing | DBUs per workload (serverless or classic compute) | Bytes scanned or slot capacity |
| Governance | Unity Catalog across data and AI assets | IAM, Dataplex, policy tags |
| ML and AI | MLflow, model serving, agents, vector search | BigQuery ML, Vertex AI |
| Open formats | Delta Lake, Iceberg interoperability | BigLake with Iceberg |
What usually decides it
- Who does the work. SQL analysts are productive fastest on BigQuery; engineers and data scientists on Databricks.
- AI ambitions. Training and serving your own models on governed data favours Databricks.
- Data types. Files, events, documents and streaming favour the lakehouse.
- Cloud strategy. Google Cloud only favours BigQuery’s integration; multi-cloud favours Databricks.
- Cost profile. Spiky, ad-hoc SQL can be cheap on BigQuery on-demand; heavy engineering workloads are often cheaper on Databricks job compute. Model both.
The short version
SQL-first, Google-centric and low-operations points to BigQuery; engineering-led, ML-heavy, streaming or multi-cloud points to Databricks; many organizations use both with federation and open formats.
Moving across? See BigQuery to Databricks. Compare all platforms with the platform chooser or read Snowflake vs BigQuery.
Not sure where your own platform stands? Take the free 3-minute data platform scorecard, or see how we work on Databricks. Unfamiliar terms are explained in the data engineering glossary.