BigQuery is a fully managed data warehouse optimized for SQL analytics with pay-per-query pricing, while Databricks is a unified analytics platform built on Apache Spark offering data engineering, ML, and analytics with flexible compute management. BigQuery suits teams wanting simplicity; Databricks suits organizations needing multi-workload flexibility.
Databricks vs BigQuery — Side-by-side comparison on A Versus B
Unified analytics platform built on Apache Spark with lakehouse architecture for data engineering, analytics, and ML.
Google's fully managed, serverless data warehouse with columnar storage optimized for SQL analytics at scale.
Architecture. Pricing Model. Setup & Management. Multi-language Support. Machine Learning Integration. Query Performance (1TB scan). Data Format Support
Choose BigQuery if your team prioritizes ease of deployment, predictable costs per query, and don't need advanced ML or multi-language engineering workflows. Choose Databricks if you need unified data engineering and ML, want to avoid vendor lock-in with open formats (Delta Lake), or require complex transformations across Python, Scala, and SQL in a single platform.
Databricks: $1,500-$4,000 | BigQuery: N/A
Databricks: All formats (Delta, Parquet, Images, Videos, Audio) | BigQuery: N/A
Databricks: Delta Sharing (open standard) | BigQuery: N/A
Databricks: SQL, Python, Scala, R, Java | BigQuery: N/A
Databricks: AWS, Azure, GCP | BigQuery: N/A
Databricks: $0.30 (1 DBU compute) | BigQuery: N/A
Databricks: 12+ model families via AutoML | BigQuery: N/A
Databricks: Unlimited simultaneous editing | BigQuery: N/A
Databricks: 15+ (S3, RDS, Kinesis) | BigQuery: N/A
Databricks: Native Delta Lake engine | BigQuery: N/A
Databricks: Up to 70% savings | BigQuery: N/A
SQL Query Performance (sample 1TB table)
Databricks: 8-15 (native optimizations) | BigQuery: N/A
Databricks: $2,000-$15,000/month | BigQuery: N/A
Databricks: Minimal (<5 hours/month) | BigQuery: N/A
Databricks: Unlimited with real-time sync | BigQuery: N/A
Databricks: 6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation) | BigQuery: N/A
Databricks: All Spark formats + native Delta Lake optimization | BigQuery: N/A
Databricks: 8,000+ questions | BigQuery: N/A
Databricks: ANSI SQL with Spark extensions | BigQuery: N/A
Databricks: 15+ native connectors | BigQuery: N/A
Databricks: MLflow, Feature Store, AutoML included | BigQuery: N/A
Databricks: Yes, into Delta Lake | BigQuery: N/A
Databricks: 1-5 seconds (on cached data) | BigQuery: N/A
Databricks: 10x (Photon engine) | BigQuery: N/A
Databricks: 25+ integrated | BigQuery: N/A
BigQuery/Equivalent Query Speed (1TB dataset)
Databricks: 15-30 sec (via Databricks SQL) | BigQuery: N/A
Databricks: MLflow + Databricks Intelligence Engine (built-in) | BigQuery: N/A
Databricks: Native Delta Lake with ACID, time travel, schema evolution | BigQuery: N/A
Databricks: Cloud-only (3 regions) | BigQuery: N/A
Databricks: Native MLflow Feature Store included | BigQuery: N/A
Data Governance (Unity Catalog equivalent)
Databricks: Unity Catalog with lineage, tags, access control | BigQuery: N/A
Databricks: 5-15 seconds | BigQuery: 1-3 seconds
Databricks: Structured, semi-structured, unstructured | BigQuery: Structured (relational tables)
Databricks: 3 (AWS, Azure, GCP) | BigQuery: 1 (Google Cloud only)
Databricks: Manual cluster setup and scaling | BigQuery: Fully serverless, automatic scaling
Databricks: MLflow, Spark MLlib, TensorFlow, PyTorch | BigQuery: BigQuery ML (SQL-based only)
Minimum Learning Curve (months for competency)
Databricks: 2-3 months | BigQuery: 2-4 weeks
Starting Monthly Cost (1 TB storage + compute)
Databricks: $400-800 (variable compute) | BigQuery: $25-150 (on-demand queries)
Databricks: 10-100x faster (Photon engine) | BigQuery: N/A
Databricks: ~15 core data/AI services | BigQuery: N/A
Databricks: $0.50-$2.50 (depends on cloud provider) | BigQuery: N/A
Databricks: MLflow, Feature Store, Model Registry | BigQuery: N/A
Databricks: Limited (Power BI via connector only) | BigQuery: N/A
Databricks: Unity Catalog, lineage, access control, Delta Lake | BigQuery: N/A
Databricks: 3-6 months (focused data/AI projects) | BigQuery: N/A
Databricks: 10-100x faster | BigQuery: N/A
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
Required Team Skills (FTE equivalents for operations)
Databricks: 0.25 (minimal management) | BigQuery: N/A
Databricks: Full ANSI SQL (100%) | BigQuery: N/A
Query Latency (median, standard ETL workload)
Databricks: 3.5-8 seconds | BigQuery: N/A
Built-in Collaboration Tools (notebooks, dashboards, repos)
Databricks: Notebooks, Dashboards, SQL Editor, Repos, MLflow | BigQuery: N/A
Databricks: 8,200 stars (databricks/databricks-cli) | BigQuery: N/A
Databricks: 3-5x faster (Photon engine) | BigQuery: N/A
Databricks: 8 frameworks (via MLflow ecosystem) | BigQuery: N/A
Databricks: Column, row, and table-level with tags | BigQuery: N/A
Databricks: 60+ (via partner clouds) | BigQuery: N/A
Databricks: 50+ (data/AI focused) | BigQuery: N/A
Databricks: $3,500-$5,000 | BigQuery: N/A
Databricks: Native (Delta Lake) | BigQuery: No (append-only snapshots)
Databricks: 8-15 seconds | BigQuery: N/A
Databricks: Native (MLflow, AutoML, Feature Store) | BigQuery: N/A
Databricks: Delta, Parquet, ORC, CSV, JSON (open-source native) | BigQuery: Parquet, Avro, CSV, JSON, ORC, Iceberg
Databricks: Unlimited (serverless SQL analytics) | BigQuery: N/A
Databricks: 15-30 minutes | BigQuery: N/A
Databricks: 18% of lakehouse market | BigQuery: N/A
Databricks: 1.0x baseline (integrated ML platform) | BigQuery: N/A
Starting Monthly Cost (10GB active data)
SQL Query Performance (TPC-DS Benchmark)
Databricks: MLflow + Spark ML | BigQuery: N/A
Databricks: Low (open Delta/Iceberg formats) | BigQuery: N/A
Maximum Concurrent Queries Per Warehouse
Databricks: Unlimited (Spark clusters) | BigQuery: N/A
Databricks: 0.25 weeks (15 minutes) | BigQuery: N/A
Databricks: 50-100x faster (Spark + Photon) | BigQuery: N/A
Databricks: 0.5-3 seconds (Photon) | BigQuery: N/A
Annual Cost (100TB/year, 5-node baseline)
Databricks: Yes (Unity Catalog native) | BigQuery: N/A
Databricks: Automatic (5-30 min provisioning) | BigQuery: N/A
Collaborative Notebooks with Version Control
Databricks: Yes (built-in with Git integration) | BigQuery: N/A
Databricks: No (cloud-only) | BigQuery: N/A
Databricks: 8-12 seconds | BigQuery: 4-6 seconds
Databricks: 30-60 minutes (cluster creation) | BigQuery: 5-10 minutes (serverless)
Databricks: $2,400-3,600 (on-demand, m5.large cluster) | BigQuery: $800-1,200 (on-demand SQL warehouse)
Databricks: SQL, Python, Scala, R, Java (full support) | BigQuery: SQL primary; Python/R in notebooks only
Databricks: Native MLflow, Feature Store, AutoML, Model Registry | BigQuery: Vertex AI integration, limited native ML tools
Databricks: 24 hours (configurable) | BigQuery: 6 hours (maximum)
Databricks: N/A | BigQuery: 1 (GCP only)
Databricks: N/A | BigQuery: 5-10 minutes
Databricks: N/A | BigQuery: ~200 datasets
Databricks: N/A | BigQuery: Unlimited with auto-scaling
Total Cost of Ownership (Annual, 100TB dataset)
Databricks: N/A | BigQuery: $78,000-$156,000
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
Databricks: N/A | BigQuery: 1000+ TB (unlimited)
Databricks: N/A | BigQuery: 1000+ simultaneous
Databricks: N/A | BigQuery: Yes (BigQuery ML with 15+ model types)
Databricks: N/A | BigQuery: N/A (Google proprietary)
Query Latency (1 billion rows, simple SELECT)
Databricks: N/A | BigQuery: $0 (pay-per-query, ~$100/month typical)
Databricks: N/A | BigQuery: 1 (Google Cloud only)
Databricks: N/A | BigQuery: 2-5 (automatic)
Databricks: N/A | BigQuery: ANSI SQL-2011 standard
Databricks: N/A | BigQuery: Unlimited (auto-scaling)
Databricks: N/A | BigQuery: Yes (BigQuery ML)
Databricks: N/A | BigQuery: Supported but not optimized
How to choose between Databricks and BigQuery
Databricks vs BigQuery — Structured Comparison Data
Machine-readable comparison dataset for Databricks vs BigQuery. Contains 139 attribute comparisons, 5 FAQ pairs.
Databricks vs BigQuery — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Databricks vs BigQuery.
Which is cheaper: Databricks or BigQuery?
BigQuery is generally cheaper for query-intensive, short-running analytics workloads (pay-per-TB scanned: $6-8/TB). Databricks is competitive for data engineering workloads with sustained compute (clusters run longer). For a typical 10TB/month analytics workload, BigQuery costs ~$60-80/month; Databricks with a small cluster runs $500-1,500/month. However, Databricks offers annual commitment discounts (20-30% off) that can improve unit economics.
Can I use Python and R in both platforms?
Databricks natively supports Python, R, Scala, and SQL with full interoperability in notebooks. BigQuery supports Python/R only in third-party notebooks (Colab, Jupyter) or via client libraries—not in the primary SQL interface. If you need Python/R as first-class citizens, Databricks is superior.
Which is better for machine learning projects?
Databricks is purpose-built for ML with MLflow, Feature Store, AutoML, and model registry all integrated. BigQuery requires integrating external tools (Vertex AI, TensorFlow) for ML workflows. Databricks' unified platform reduces data movement and is preferred by ML teams.
How much faster is BigQuery for analytical queries?
BigQuery is 40-60% faster on typical analytical queries (1TB scans in 4-6 seconds vs. Databricks' 8-12 seconds) due to its columnar storage and query optimization. However, Databricks excels at complex ETL and iterative workloads where Spark's distributed processing shines. For pure SQL analytics, BigQuery wins.
What happens if I want to switch platforms later?
BigQuery uses standard formats (Parquet, Avro) but tight Google Cloud integration makes migration difficult. Databricks uses Delta Lake (open-source Apache format) and is cloud-agnostic (AWS, Azure, GCP), making portability easier. If avoiding vendor lock-in is a priority, Databricks is more future-proof.
How to Choose Between Databricks and BigQuery
Architecture: Databricks has Unified lakehouse (data lake + warehouse), while BigQuery has Serverless data warehouse (columnar storage).
Pricing Model: Databricks has $6-8 per TB scanned (on-demand); $40k-60k/year (annual slots), while BigQuery has $0.04-0.08 per compute hour; clusters + storage separate.
Setup & Management: Databricks has Fully serverless, no infrastructure management required, while BigQuery has Requires cluster configuration and compute management.
Multi-language Support: Databricks has SQL primary; Python, Scala, R via PySpark (notebook-based), while BigQuery has SQL-first; limited Python/R support in standard interface.
Machine Learning Integration: Databricks has Native MLflow, AutoML, Feature Store included, while BigQuery has Vertex AI integration; less native ML tooling.
Query Performance (1TB scan): Databricks has 5-15 seconds average (depends on data skew), while BigQuery has 3-8 seconds average (optimized columnar).
Data Format Support: Databricks has Delta, Parquet, CSV, JSON; open-source formats, while BigQuery has Parquet, Avro, CSV, JSON; proprietary optimizations.