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Databricks SQL vs Google BigQuery

Databricks SQL: Serverless SQL warehouses on the Databricks lakehouse, powered by the Photon C++ engine. Google BigQuery: Google's serverless, pay-per-query data warehouse built on Dremel and Colossus.

key differences

  • Only Google BigQuery has full-text search, stored procedures, ttl / expiry.

choose databricks-sql for

  • +Lakehouse SQL and BI on Delta Lake
  • +Consolidating a separate warehouse into Databricks
  • +dbt transformations on the lakehouse
  • +Serving features and aggregates for ML

best at · Lakehouse, Warehouse, OLAP

choose google-bigquery for

  • +Cloud data warehouse on GCP
  • +Marketing and product analytics (GA4 export)
  • +Log and event analytics
  • +Ad-hoc exploration of very large datasets

best at · Warehouse, OLAP, Lakehouse

Databricks SQLServerless SQL warehouses on the Databricks lakehouse, powered by the Photon C++ engine.
Google BigQueryGoogle's serverless, pay-per-query data warehouse built on Dremel and Colossus.
overview
Primary model
Columnar / OLAP
Columnar / OLAP
Also supports
Relational, Document, Vector, Spatial
Relational, Document, Vector, Spatial
Type
Managed service
Managed service
Status
Active
Active
Developer
Databricks
Google
First release
2020
2010
Latest version
–
–
License
ProprietaryProprietary
ProprietaryProprietary
Free tier
✓
✓
popularity
WhichDB Score
20.8
65.3
Rank
#147
#12
GitHub stars
–
–
Stack Overflow questions
543
26.1K
scale & guarantees
Comfortable data size
Massive (Multi-PB)
Massive (Multi-PB)
Horizontal scaling
Native
Native
Multi-region
None
Read replicas
ACID
Full
Full
Consistency
Strong
Strong
Schema
Flexible
Flexible
Architecture
Serverless, Distributed, Shared storage
Serverless, Distributed, Shared storage
deployment
Self-hosted
—
—
Vendor-managed cloud
✓
✓
Serverless
✓
✓
Embedded
—
—
Edge
—
—
Clouds
AWS, Azure, Google Cloud
Google Cloud
capabilities
Transactions
✓
✓
Joins
✓
✓
Secondary indexes
—
—
Full-text search
—
✓
Vector search
✓
✓
Geospatial
✓
✓
JSON
✓
✓
Change data capture
✓
✓
Triggers
—
—
Stored procedures
—
✓
Materialized views
✓
✓
TTL / expiry
—
✓
Pub/Sub
—
—
Columnar storage
✓
✓
In-memory
—
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
✓
✓
Role-based access
✓
✓
Time travel
✓
✓
Branching
—
—
Online schema changes
✓
✓
Streaming ingest
✓
✓
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
SQL (Databricks SQL / ANSI), Python
GoogleSQL, Legacy SQL, Python (BigQuery DataFrames)
Client languages
Python, Java, JavaScript, Go, .NET
Python, Java, Go, JavaScript, C#, Ruby, PHP
Best for
Lakehouse, Warehouse, OLAP
Warehouse, OLAP, Lakehouse, Real-time analytics
Add more to this comparisonNot sure either fits? Try smart match

Databricks SQL vs Google BigQuery: FAQ

What is the difference between Databricks SQL and Google BigQuery?

Databricks SQL: Serverless SQL warehouses on the Databricks lakehouse, powered by the Photon C++ engine. Google BigQuery: Google's serverless, pay-per-query data warehouse built on Dremel and Colossus. Only Google BigQuery has full-text search, stored procedures, ttl / expiry.

When should I choose Databricks SQL over Google BigQuery?

Choose Databricks SQL for lakehouse SQL and BI on Delta Lake; consolidating a separate warehouse into Databricks; and dbt transformations on the lakehouse. Choose Google BigQuery for cloud data warehouse on GCP; marketing and product analytics (GA4 export); and log and event analytics.

Is Databricks SQL or Google BigQuery more popular?

Google BigQuery ranks #12 on the WhichDB Score (65.3/100) and Databricks SQL ranks #147 (20.8/100). Popularity is not fit: check the requirements that matter for your project.

Are Databricks SQL and Google BigQuery open source?

No. Databricks SQL is proprietary, closed-source software. No. Google BigQuery is proprietary, closed-source software.