Independent comparison for technology buyers. Updated May 2026.
Quick verdict: Choose Databricks for lakehouse workloads spanning ETL, ML/AI on Spark, and BI on Delta tables across AWS, Azure, and GCP. Choose Google BigQuery when running on Google Cloud, when serverless SQL on a managed warehouse fits the workload, or when tight Vertex AI and Looker integration is preferred. The differentiator is open lakehouse on Delta with full ML/AI tooling versus serverless SQL warehouse with native GCP AI tie-ins.
| Criteria | Databricks | Google BigQuery |
|---|---|---|
| Rating | 4.6 / 5.0 (3,200 reviews) | 4.5 / 5.0 (2,400 reviews) |
| Architecture | Lakehouse on Delta / Spark, multi-cloud | Serverless SQL warehouse, slot-based |
| Cloud Deployment | AWS, Azure, GCP | Google Cloud only |
| Pricing Model | DBUs per cluster type + cloud infrastructure | Per-TB scanned on-demand + reserved slots |
| Open Format | Delta Lake, Iceberg via UniForm | Native columnar (Capacitor), Iceberg read |
| ML / AI | MLflow, Mosaic AI, AutoML, model serving | BigQuery ML, Vertex AI integration |
| BI Integration | Databricks SQL, Power BI, Tableau | Looker native, Power BI, Tableau |
| Best For | ETL, ML/AI, lakehouse workloads | Serverless analytics on GCP, AI on Vertex |
| Real-Time | Structured Streaming, Delta Live Tables | BigQuery streaming inserts, subscriptions |
Databricks delivers a lakehouse platform combining Spark-based ETL, ML training and serving, and SQL analytics on Delta Lake tables. The Photon engine accelerates SQL workloads on Delta to performance levels comparable with cloud data warehouses while preserving the open lakehouse architecture. Unity Catalog provides governance across data and AI assets, including model lineage and feature store. Mosaic AI extends the platform with model training infrastructure and managed model serving.
Google BigQuery is a fully managed serverless data warehouse on Google Cloud. Storage uses a proprietary columnar format (Capacitor); compute runs on slots from a shared pool or reservations. BigQuery ML allows model training in SQL; Vertex AI integration brings Gemini and managed ML services into the data layer. BigLake extends BigQuery to read Iceberg and Delta tables in Cloud Storage, providing a lakehouse path.
For ML/AI-heavy workloads, open Delta architecture, or multi-cloud lakehouse strategy, Databricks tends to fit better. For serverless SQL analytics on Google Cloud with Vertex AI and Looker, BigQuery is the more natural choice. See Snowflake vs Databricks and the data analytics category.
Databricks pricing combines DBU rates (workload type and tier dependent, roughly $0.07-$0.95 per DBU) with the underlying cloud VM and storage. Photon clusters and All-Purpose compute cost more than Jobs compute. Enterprise spend commonly lands $300,000-$10M ARR including cloud infrastructure.
BigQuery on-demand pricing is approximately $6.25/TB scanned; reserved slot editions (Standard, Enterprise, Enterprise Plus) are priced per slot-hour with annual commitments at significant discount. Active storage runs around $20/TB/month, long-term storage around $10/TB/month. Enterprise BigQuery spend typically lands $200,000-$8M ARR.
Choose Databricks when machine learning, data science, and AI workloads share the same platform with ETL and BI, when an open Delta or Iceberg lakehouse is a strategic direction, or when multi-cloud deployment is required across AWS, Azure, and GCP.
Choose BigQuery when running primarily on Google Cloud, when serverless SQL with per-TB-scanned billing fits workload patterns, when Vertex AI and Gemini integration drives ML strategy, or when Looker is the BI platform of record.