software
Databricks vs BigQuery 2026: Comparison
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
Unified analytics platform built on Apache Spark with lakehouse architecture for data engineering, analytics, and ML.
Data engineers, ML teams, and organizations needing unified data lakehouse with multi-language support and advanced analytics workflows
Score63%
VS
B
BigQuery
Google's fully managed, serverless data warehouse with columnar storage optimized for SQL analytics at scale.
SQL-focused analytics teams, business intelligence users, and organizations seeking plug-and-play data warehouse without infrastructure management
Score63%
Quick Answer
AI SummaryBigQuery 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.
Our Verdict
AI-assistedChoose 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.
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Databricks
6.1/10
BigQuery
8.9/10
B
Choose Databricks if
Data engineers, ML teams, and organizations needing unified data lakehouse with multi-language support and advanced analytics workflows
B
Choose BigQuery if
Best pickSQL-focused analytics teams, business intelligence users, and organizations seeking plug-and-play data warehouse without infrastructure management
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Key Differences at a Glance
- Architecture:Unified lakehouse (data lake + warehouse) vs Serverless data warehouse (columnar storage)
- Pricing Model:✓ BigQuery wins($0.04-0.08 per compute hour; clusters + storage separate vs $6-8 per TB scanned (on-demand); $40k-60k/year (annual slots))
- Setup & Management:✓ Databricks wins(Fully serverless, no infrastructure management required vs Requires cluster configuration and compute management)
Key Facts & Figures
91 numeric metrics compared
| Metric | Databricks | BigQuery | Ratio |
|---|---|---|---|
| Starting Monthly Cost(USD) | $1,500-$4,000 | — | — |
| Setup Time(minutes) | 3-7 days | — | — |
| Query Performance (TPC-DS)(seconds) | 18-25 | — | — |
| ML/AI Integration Score(out of 10) | 9/10 | — | — |
| Global Enterprise Customers(count (2026)) | 6,500+ | — | — |
| Starting Compute Cost (per hour)(USD) | $0.30 (1 DBU compute) | — | — |
| Pre-built AutoML Models(models) | 12+ model families via AutoML | — | — |
| Native AWS Service Integrations(services) | 15+ (S3, RDS, Kinesis) | — | — |
| Training Job Spot Instance Discount(%) | Up to 70% savings | — | — |
| SQL Query Performance (sample 1TB table)(seconds) | 8-15 (native optimizations) | — | — |
| Setup Time to Production(minutes) | 1-2 weeks | — | — |
| Starting Monthly Cost (Small Team)(USD) | $500-2,000 | — | — |
| Supported Data Connectors(count) | 15+ native connectors | — | — |
| Enterprise SLA Uptime(percent) | 99.9% | — | — |
| Average Query Latency (Analytical)(seconds) | 1-5 seconds (on cached data) | — | — |
| Time to Deploy (Basic Setup)(days) | 3-7 days | — | — |
| Monthly Starting Cost(USD) | $600-900 | — | — |
| Apache Spark Query Performance Boost(x faster vs open-source) | 10x (Photon engine) | — | — |
| Available Services(count) | 25+ integrated | — | — |
| BigQuery/Equivalent Query Speed (1TB dataset)(seconds) | 15-30 sec (via Databricks SQL) | — | — |
| Organizations Using Platform(count (thousands)) | 30,000+ | — | — |
| Enterprise Customers(millions) | 10,000+ | — | — |
| Query Latency (Average)(milliseconds) | 40-100 ms | — | — |
| Compute Cost Per Hour(USD) | $0.40-0.50 | — | — |
| Setup Complexity (1=Simple, 10=Complex)(scale) | 7/10 | — | — |
| Typical Query Latency (Structured Data)(seconds) | 5-15 seconds | 1-3 seconds | |
| Cloud Providers(count) | 3 (AWS, Azure, GCP) | 1 (Google Cloud only) | |
| Minimum Learning Curve (months for competency)(months) | 2-3 months | 2-4 weeks | |
| Starting Monthly Cost (1 TB storage + compute)(USD) | $400-800 (variable compute) | $25-150 (on-demand queries) | |
| Spark Performance (Query Speed)(x faster relative to standard Spark) | 10-100x faster (Photon engine) | — | — |
| Total Service Offerings(services) | ~15 core data/AI services | — | — |
| Compute Instance Cost (Standard)(USD per hour) | $0.50-$2.50 (depends on cloud provider) | — | — |
| Typical Enterprise Migration Time(months) | 3-6 months (focused data/AI projects) | — | — |
| Initial Setup Time to Production(weeks) | 1-2 weeks | — | — |
| Processing Speed vs MapReduce Baseline(times faster) | 10-100x faster | — | — |
| Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) | $550-850 | — | — |
| Required Team Skills (FTE equivalents for operations)(FTE) | 0.25 (minimal management) | — | — |
| SQL Query Standards Compliance(% ANSI SQL support) | Full ANSI SQL (100%) | — | — |
| Query Latency (median, standard ETL workload)(seconds) | 3.5-8 seconds | — | — |
| Built-in Collaboration Tools (notebooks, dashboards, repos)(count) | Notebooks, Dashboards, SQL Editor, Repos, MLflow | — | — |
| Community Size (GitHub stars)(stars) | 8,200 stars (databricks/databricks-cli) | — | — |
| SQL Query Performance (TPC-DS 100TB)(seconds) | 285 seconds | — | — |
| Spark Job Acceleration(multiplier) | 3-5x faster (Photon engine) | — | — |
| ML Frameworks Supported(count) | 8 frameworks (via MLflow ecosystem) | — | — |
| Global Region Availability(regions) | 60+ (via partner clouds) | — | — |
| Enterprise Service Count(services) | 50+ (data/AI focused) | — | — |
| Starting Monthly Cost (10TB workload)(USD) | $3,500-$5,000 | — | — |
| SQL Query Performance (1TB dataset)(seconds) | 8-15 seconds | — | — |
| Base Monthly Cost (minimum)(USD) | $500+ | — | — |
| Concurrent Users Support(users) | Unlimited (serverless SQL analytics) | — | — |
| Data Warehouse Setup Time(minutes) | 15-30 minutes | — | — |
| Global Market Share (2024)(%) | 18% of lakehouse market | — | — |
| ML Model Training Cost Efficiency(relative cost index) | 1.0x baseline (integrated ML platform) | — | — |
| Starting Monthly Cost (10GB active data)(USD) | $650 | — | — |
| SQL Query Performance (TPC-DS Benchmark)(seconds) | 45 | — | — |
| BI Tool Native Connectors(count) | 65 | — | — |
| Customer Satisfaction Rating (G2 2025)(percent) | 82% | — | — |
| Setup Complexity (1-10 scale)(difficulty score) | 7 | — | — |
| Initial Deployment Time(minutes) | 0.25 weeks (15 minutes) | — | — |
| Processing Speed (Iterative ML)(x relative to baseline) | 50-100x faster (Spark + Photon) | — | — |
| SQL Query Latency (100GB dataset)(seconds) | 0.5-3 seconds (Photon) | — | — |
| Annual Cost (100TB/year, 5-node baseline)(USD thousands) | $120,000-$180,000 | — | — |
| Query Latency (1TB dataset scan)(seconds) | 8-12 seconds | 4-6 seconds | |
| Setup Time to First Query(minutes) | 30-60 minutes (cluster creation) | 5-10 minutes (serverless) | |
| Compute Cost (1000 compute hours/month)(USD) | $2,400-3,600 (on-demand, m5.large cluster) | $800-1,200 (on-demand SQL warehouse) | |
| Storage Cost per TB/month(USD) | $0.03-0.05/GB ($30-50/TB) | $0.02/GB ($20/TB) | |
| Maximum Query Timeout(hours) | 24 hours (configurable) | 6 hours (maximum) | |
| Base Query Cost(USD per TB scanned) | $6.25 | $6.25 | |
| Supported Cloud Providers(number of platforms) | 1 (GCP only) | 1 (GCP only) | |
| Setup Time to First Query(minutes) | 5-10 minutes | 5-10 minutes | |
| Maximum Query Timeout(hours) | 24 hours | 24 hours | |
| Data Marketplace Size(number of datasets) | ~200 datasets | ~200 datasets | |
| Annual Customer Growth Rate (2025)(percent) | 18% YoY | 18% YoY | |
| Average Enterprise Contract Value(USD thousands per year) | $150-300 | $150-300 | |
| Total Cost of Ownership (Annual, 100TB dataset)(USD) | $78,000-$156,000 | $78,000-$156,000 | |
| Query Latency (10GB dataset, simple aggregate)(seconds) | 1-2 seconds | 1-2 seconds | |
| Query Latency (1TB dataset, complex join)(seconds) | 1-2 seconds | 1-2 seconds | |
| Maximum Supported Dataset Size(TB) | 1000+ TB (unlimited) | 1000+ TB (unlimited) | |
| Concurrent User Queries(users) | 1000+ simultaneous | 1000+ simultaneous | |
| Query Latency (1 billion rows, simple SELECT)(milliseconds) | 2,500ms | 2,500ms | |
| Cost per GB Scanned(USD) | $0.0275 | $0.0275 | |
| Maximum Ingestion Rate(events/second) | 1,000,000 | 1,000,000 | |
| Infrastructure Management Overhead(hours per month) | 0-5 hours | 0-5 hours | |
| Minimum Monthly Cost (basic setup)(USD) | $0 (pay-per-query, ~$100/month typical) | $0 (pay-per-query, ~$100/month typical) | |
| Cloud Provider Support(providers) | 1 (Google Cloud only) | 1 (Google Cloud only) | |
| Automatic Scaling Time(seconds) | 2-5 (automatic) | 2-5 (automatic) | |
| Average Query Latency (1TB dataset)(milliseconds) | 3,200ms | 3,200ms | |
| Cost per TB Scanned(USD) | $6.25 | $6.25 | |
| Initial Setup Time(minutes) | 0.25 days | 0.25 days | |
| Data Compression Ratio(x compression) | 5:1 average | 5:1 average | |
| Enterprise SLA Availability(percent) | 99.99% | 99.99% |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Databricks leads1 tie
B
2BigQueryDatabricks
Attribute
BigQuery
- Unified lakehouse (data lake + warehouse)ArchitectureServerless data warehouse (columnar storage)
- $6-8 per TB scanned (on-demand); $40k-60k/year (annual slots)Pricing Model$0.04-0.08 per compute hour; clusters + storage separate(winner)
- Fully serverless, no infrastructure management required(winner)Setup & ManagementRequires cluster configuration and compute management
- SQL primary; Python, Scala, R via PySpark (notebook-based)(winner)Multi-language SupportSQL-first; limited Python/R support in standard interface
- Native MLflow, AutoML, Feature Store included(winner)Machine Learning IntegrationVertex AI integration; less native ML tooling
- 5-15 seconds average (depends on data skew)Query Performance (1TB scan)3-8 seconds average (optimized columnar)(winner)
- Delta, Parquet, CSV, JSON; open-source formats(winner)Data Format SupportParquet, Avro, CSV, JSON; proprietary optimizations
- Architecture
Databricks
Unified lakehouse (data lake + warehouse)
BigQuery
Serverless data warehouse (columnar storage)
- Pricing Model
Databricks
$6-8 per TB scanned (on-demand); $40k-60k/year (annual slots)
BigQuery
$0.04-0.08 per compute hour; clusters + storage separate(winner)
- Setup & Management
Databricks
Fully serverless, no infrastructure management required(winner)
BigQuery
Requires cluster configuration and compute management
- Multi-language Support
Databricks
SQL primary; Python, Scala, R via PySpark (notebook-based)(winner)
BigQuery
SQL-first; limited Python/R support in standard interface
- Machine Learning Integration
Databricks
Native MLflow, AutoML, Feature Store included(winner)
BigQuery
Vertex AI integration; less native ML tooling
- Query Performance (1TB scan)
Databricks
5-15 seconds average (depends on data skew)
BigQuery
3-8 seconds average (optimized columnar)(winner)
- Data Format Support
Databricks
Delta, Parquet, CSV, JSON; open-source formats(winner)
BigQuery
Parquet, Avro, CSV, JSON; proprietary optimizations
Full Comparison
| Attribute | Databricks | BigQuery |
|---|---|---|
| Starting Monthly Cost(USD) | $1,500-$4,000 | — |
| Starting Compute Cost (per hour)(USD) | $0.30 (1 DBU compute) | — |
| Starting Monthly Cost (Small Team)(USD) | $500-2,000 | — |
| Monthly Starting Cost(USD) | $600-900 | — |
| Compute Cost Per Hour(USD) | $0.40-0.50 | — |
Show 13 more attributesStarting Monthly Cost (1 TB storage + compute)(USD) $400-800 (variable compute) $25-150 (on-demand queries) Compute Instance Cost (Standard)(USD per hour) $0.50-$2.50 (depends on cloud provider) — Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) $550-850 — Starting Monthly Cost (10TB workload)(USD) $3,500-$5,000 — Base Monthly Cost (minimum)(USD) $500+ — Starting Monthly Cost (10GB active data)(USD) $650 — Compute Cost (1000 compute hours/month)(USD) $2,400-3,600 (on-demand, m5.large cluster) $800-1,200 (on-demand SQL warehouse) Storage Cost per TB/month(USD) $0.03-0.05/GB ($30-50/TB) $0.02/GB ($20/TB) Base Query Cost(USD per TB scanned) $6.25 — Average Enterprise Contract Value(USD thousands per year) $150-300 — Cost per GB Scanned(USD) $0.0275 — Minimum Monthly Cost (basic setup)(USD) $0 (pay-per-query, ~$100/month typical) — Cost per TB Scanned(USD) $6.25 — | ||
| Setup Time(minutes) | 3-7 days | — |
| Customer Satisfaction Rating (G2 2025)(percent) | 82% | — |
| Query Performance (TPC-DS)(seconds) | 18-25 | — |
| SQL Query Performance (sample 1TB table)(seconds) | 8-15 (native optimizations) | — |
| Average Query Latency (Analytical)(seconds) | 1-5 seconds (on cached data) | — |
| Apache Spark Query Performance Boost(x faster vs open-source) | 10x (Photon engine) | — |
| BigQuery/Equivalent Query Speed (1TB dataset)(seconds) | 15-30 sec (via Databricks SQL) | — |
Show 21 more attributesQuery Latency (Average)(milliseconds) 40-100 ms — Typical Query Latency (Structured Data)(seconds) 5-15 seconds 1-3 seconds Spark Performance (Query Speed)(x faster relative to standard Spark) 10-100x faster (Photon engine) — Processing Speed vs MapReduce Baseline(times faster) 10-100x faster — Query Latency (median, standard ETL workload)(seconds) 3.5-8 seconds — SQL Query Performance (TPC-DS 100TB)(seconds) 285 seconds — Spark Job Acceleration(multiplier) 3-5x faster (Photon engine) — SQL Query Performance (1TB dataset)(seconds) 8-15 seconds — SQL Query Performance (TPC-DS Benchmark)(seconds) 45 — Maximum Concurrent Queries Per Warehouse(queries) Unlimited (Spark clusters) — Processing Speed (Iterative ML)(x relative to baseline) 50-100x faster (Spark + Photon) — SQL Query Latency (100GB dataset)(seconds) 0.5-3 seconds (Photon) — Query Latency (1TB dataset scan)(seconds) 8-12 seconds 4-6 seconds Maximum Query Timeout(hours) 24 hours (configurable) 6 hours (maximum) Maximum Query Timeout(hours) 24 hours — Concurrent User Support(scalability level) Unlimited with auto-scaling — Query Latency (10GB dataset, simple aggregate)(seconds) 1-2 seconds — Query Latency (1TB dataset, complex join)(seconds) 1-2 seconds — Query Latency (1 billion rows, simple SELECT)(milliseconds) 2,500ms — Automatic Scaling Time(seconds) 2-5 (automatic) — Average Query Latency (1TB dataset)(milliseconds) 3,200ms — | ||
| ML/AI Integration Score(out of 10) | 9/10 | — |
| Native ML Framework Integration | MLflow + Spark ML | — |
| Global Enterprise Customers(count (2026)) | 6,500+ | — |
| Global Market Share (2024)(%) | 18% of lakehouse market | — |
| Supported Data Formats(types) | All formats (Delta, Parquet, Images, Videos, Audio) | — |
| Multi-Cloud Support(cloud providers) | AWS, Azure, GCP | — |
| Deployment Options | Cloud-only (3 regions) | — |
| Data Sharing Standard(technology) | Delta Sharing (open standard) | — |
| Available Services(count) | 25+ integrated | — |
| Total Service Offerings(services) | ~15 core data/AI services | — |
| Microsoft Ecosystem Integration(native integrations) | Limited (Power BI via connector only) | — |
| SQL Query Standards Compliance(% ANSI SQL support) | Full ANSI SQL (100%) | — |
Show 9 more attributesBuilt-in Collaboration Tools (notebooks, dashboards, repos)(count) Notebooks, Dashboards, SQL Editor, Repos, MLflow — Native ML/AI Capabilities Native (MLflow, AutoML, Feature Store) — Data Format Support Delta, Parquet, ORC, CSV, JSON (open-source native) Parquet, Avro, CSV, JSON, ORC, Iceberg Supported Programming Languages SQL, Python, Scala, R, Java (full support) SQL primary; Python/R in notebooks only MLOps/ML Tooling Native MLflow, Feature Store, AutoML, Model Registry Vertex AI integration, limited native ML tools Built-in Machine Learning Capabilities Yes (BigQuery ML with 15+ model types) — Cloud Provider Support(providers) 1 (Google Cloud only) — SQL Compatibility(percentage) ANSI SQL-2011 standard — Built-in ML Capabilities Yes (BigQuery ML) — | ||
| Multi-Language Support(languages) | SQL, Python, Scala, R, Java | — |
| Supported Cloud Platforms(count) | AWS, Azure, GCP | — |
| Setup Time to Production(minutes) | 1-2 weeks | — |
| Initial Setup Time to Production(weeks) | 1-2 weeks | — |
| On-Premises Deployment Option | No (cloud-only) | — |
| Setup Time to First Query(minutes) | 5-10 minutes | — |
| Pre-built AutoML Models(models) | 12+ model families via AutoML | — |
| Real-Time Notebook Collaboration Users(concurrent users) | Unlimited simultaneous editing | — |
| Users Per Collaborative Project(concurrent users) | Unlimited with real-time sync | — |
| Collaborative Notebooks with Version Control(native support) | Yes (built-in with Git integration) | — |
| Native AWS Service Integrations(services) | 15+ (S3, RDS, Kinesis) | — |
| BI Tool Native Connectors(count) | 65 | — |
| Delta Lake Support | Native Delta Lake engine | — |
| Training Job Spot Instance Discount(%) | Up to 70% savings | — |
| Initial Licensing Cost(USD) | $2,000-$15,000/month | — |
| Annual Cost (100TB/year, 5-node baseline)(USD thousands) | $120,000-$180,000 | — |
| Total Cost of Ownership (Annual, 100TB dataset)(USD) | $78,000-$156,000 | — |
| Cluster Management Required(hours/month) | Minimal (<5 hours/month) | — |
| Infrastructure Management Required(null) | Manual cluster setup and scaling | Fully serverless, automatic scaling |
| Required Team Skills (FTE equivalents for operations)(FTE) | 0.25 (minimal management) | — |
| Cluster Auto-scaling Capability(supported) | Automatic (5-30 min provisioning) | — |
| Infrastructure Management Overhead(hours per month) | 0-5 hours | — |
| Built-in Security Features | 6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation) | — |
| Supported Data Formats(formats) | All Spark formats + native Delta Lake optimization | — |
| Community Size(active users) | 8,000+ questions | — |
| SQL Standard Compliance Level(null) | ANSI SQL with Spark extensions | — |
| Supported Data Connectors(count) | 15+ native connectors | — |
| Enterprise SLA Uptime(percent) | 99.9% | — |
| Enterprise SLA Availability(percent) | 99.99% | — |
| Native ML/AI Features(null) | MLflow, Feature Store, AutoML included | — |
| ML Frameworks Supported(count) | 8 frameworks (via MLflow ecosystem) | — |
| Data Consolidation Required(null) | Yes, into Delta Lake | — |
| Time to Deploy (Basic Setup)(days) | 3-7 days | — |
| Typical Enterprise Migration Time(months) | 3-6 months (focused data/AI projects) | — |
| Initial Deployment Time(minutes) | 0.25 weeks (15 minutes) | — |
| Organizations Using Platform(count (thousands)) | 30,000+ | — |
| Fortune 500 Adoption(%) | 40% | — |
| Native ML Pipeline Integration(rating) | MLflow + Databricks Intelligence Engine (built-in) | — |
| Data Lakehouse ACID Support(capability) | Native Delta Lake with ACID, time travel, schema evolution | — |
| Data Governance Features(key capabilities) | Unity Catalog, lineage, access control, Delta Lake | — |
| Enterprise Customers(millions) | 10,000+ | — |
| Annual Customer Growth Rate (2025)(percent) | 18% YoY | — |
| ML Feature Store(null) | Native MLflow Feature Store included | — |
| Native ML Framework Support | MLflow, Spark MLlib, TensorFlow, PyTorch | BigQuery ML (SQL-based only) |
| Native ML Ops Tools(tools included) | MLflow, Feature Store, Model Registry | — |
| Data Governance (Unity Catalog equivalent)(null) | Unity Catalog with lineage, tags, access control | — |
| Native Row/Column-Level Access Control(supported) | Yes (Unity Catalog native) | — |
| Setup Complexity (1=Simple, 10=Complex)(scale) | 7/10 | — |
| Setup Complexity (1-10 scale)(difficulty score) | 7 | — |
| Supported Data Types(types) | Structured, semi-structured, unstructured | Structured (relational tables) |
| Cloud Providers(count) | 3 (AWS, Azure, GCP)(winner) | 1 (Google Cloud only) |
| Global Region Availability(regions) | 60+ (via partner clouds) | — |
| Supported Cloud Providers(number of platforms) | 1 (GCP only) | — |
| Minimum Learning Curve (months for competency)(months) | 2-3 months | 2-4 weeks(winner) |
| Data Warehouse Setup Time(minutes) | 15-30 minutes | — |
| Setup Time to First Query(minutes) | 30-60 minutes (cluster creation) | 5-10 minutes (serverless)(winner) |
| Initial Setup Time(minutes) | 0.25 days | — |
| Community Size (GitHub stars)(stars) | 8,200 stars (databricks/databricks-cli) | — |
| Data Governance Granularity(access level) | Column, row, and table-level with tags | — |
| Enterprise Service Count(services) | 50+ (data/AI focused) | — |
| ACID Transaction Support(boolean) | Native (Delta Lake) | No (append-only snapshots) |
| Concurrent Users Support(users) | Unlimited (serverless SQL analytics) | — |
| Maximum Supported Dataset Size(TB) | 1000+ TB (unlimited) | — |
| Concurrent User Queries(users) | 1000+ simultaneous | — |
| Max Concurrent Queries (single cluster)(queries) | Unlimited (auto-scaling) | — |
| ML Model Training Cost Efficiency(relative cost index) | 1.0x baseline (integrated ML platform) | — |
| Data Format Lock-in Risk | Low (open Delta/Iceberg formats) | — |
| Data Marketplace Size(number of datasets) | ~200 datasets | — |
| GitHub Stars (Community Traction)(thousands) | N/A (Google proprietary) | — |
| Maximum Ingestion Rate(events/second) | 1,000,000 | — |
| Data Compression Ratio(x compression) | 5:1 average | — |
| Support for Time-Series Data | Supported but not optimized | — |
DatabricksBBigQuery
Starting Monthly Cost(USD)
$1,500-$4,000
—
Starting Compute Cost (per hour)(USD)
$0.30 (1 DBU compute)
—
Starting Monthly Cost (Small Team)(USD)
$500-2,000
—
Monthly Starting Cost(USD)
$600-900
—
Compute Cost Per Hour(USD)
$0.40-0.50
—
Show 13 more attributes
Starting Monthly Cost (1 TB storage + compute)(USD)
$400-800 (variable compute)
$25-150 (on-demand queries)
Compute Instance Cost (Standard)(USD per hour)
$0.50-$2.50 (depends on cloud provider)
—
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD)
$550-850
—
Starting Monthly Cost (10TB workload)(USD)
$3,500-$5,000
—
Base Monthly Cost (minimum)(USD)
$500+
—
Starting Monthly Cost (10GB active data)(USD)
$650
—
Compute Cost (1000 compute hours/month)(USD)
$2,400-3,600 (on-demand, m5.large cluster)
$800-1,200 (on-demand SQL warehouse)
Storage Cost per TB/month(USD)
$0.03-0.05/GB ($30-50/TB)
$0.02/GB ($20/TB)
Base Query Cost(USD per TB scanned)
$6.25
—
Average Enterprise Contract Value(USD thousands per year)
$150-300
—
Cost per GB Scanned(USD)
$0.0275
—
Minimum Monthly Cost (basic setup)(USD)
$0 (pay-per-query, ~$100/month typical)
—
Cost per TB Scanned(USD)
$6.25
—
Setup Time(minutes)
3-7 days
—
Customer Satisfaction Rating (G2 2025)(percent)
82%
—
Query Performance (TPC-DS)(seconds)
18-25
—
SQL Query Performance (sample 1TB table)(seconds)
8-15 (native optimizations)
—
Average Query Latency (Analytical)(seconds)
1-5 seconds (on cached data)
—
Apache Spark Query Performance Boost(x faster vs open-source)
10x (Photon engine)
—
BigQuery/Equivalent Query Speed (1TB dataset)(seconds)
15-30 sec (via Databricks SQL)
—
Show 21 more attributes
Query Latency (Average)(milliseconds)
40-100 ms
—
Typical Query Latency (Structured Data)(seconds)
5-15 seconds
1-3 seconds
Spark Performance (Query Speed)(x faster relative to standard Spark)
10-100x faster (Photon engine)
—
Processing Speed vs MapReduce Baseline(times faster)
10-100x faster
—
Query Latency (median, standard ETL workload)(seconds)
3.5-8 seconds
—
SQL Query Performance (TPC-DS 100TB)(seconds)
285 seconds
—
Spark Job Acceleration(multiplier)
3-5x faster (Photon engine)
—
SQL Query Performance (1TB dataset)(seconds)
8-15 seconds
—
SQL Query Performance (TPC-DS Benchmark)(seconds)
45
—
Maximum Concurrent Queries Per Warehouse(queries)
Unlimited (Spark clusters)
—
Processing Speed (Iterative ML)(x relative to baseline)
50-100x faster (Spark + Photon)
—
SQL Query Latency (100GB dataset)(seconds)
0.5-3 seconds (Photon)
—
Query Latency (1TB dataset scan)(seconds)
8-12 seconds
4-6 seconds
Maximum Query Timeout(hours)
24 hours (configurable)
6 hours (maximum)
Maximum Query Timeout(hours)
24 hours
—
Concurrent User Support(scalability level)
Unlimited with auto-scaling
—
Query Latency (10GB dataset, simple aggregate)(seconds)
1-2 seconds
—
Query Latency (1TB dataset, complex join)(seconds)
1-2 seconds
—
Query Latency (1 billion rows, simple SELECT)(milliseconds)
2,500ms
—
Automatic Scaling Time(seconds)
2-5 (automatic)
—
Average Query Latency (1TB dataset)(milliseconds)
3,200ms
—
ML/AI Integration Score(out of 10)
9/10
—
Native ML Framework Integration
MLflow + Spark ML
—
Global Enterprise Customers(count (2026))
6,500+
—
Global Market Share (2024)(%)
18% of lakehouse market
—
Supported Data Formats(types)
All formats (Delta, Parquet, Images, Videos, Audio)
—
Multi-Cloud Support(cloud providers)
AWS, Azure, GCP
—
Deployment Options
Cloud-only (3 regions)
—
Data Sharing Standard(technology)
Delta Sharing (open standard)
—
Available Services(count)
25+ integrated
—
Total Service Offerings(services)
~15 core data/AI services
—
Microsoft Ecosystem Integration(native integrations)
Limited (Power BI via connector only)
—
SQL Query Standards Compliance(% ANSI SQL support)
Full ANSI SQL (100%)
—
Show 9 more attributes
Built-in Collaboration Tools (notebooks, dashboards, repos)(count)
Notebooks, Dashboards, SQL Editor, Repos, MLflow
—
Native ML/AI Capabilities
Native (MLflow, AutoML, Feature Store)
—
Data Format Support
Delta, Parquet, ORC, CSV, JSON (open-source native)
Parquet, Avro, CSV, JSON, ORC, Iceberg
Supported Programming Languages
SQL, Python, Scala, R, Java (full support)
SQL primary; Python/R in notebooks only
MLOps/ML Tooling
Native MLflow, Feature Store, AutoML, Model Registry
Vertex AI integration, limited native ML tools
Built-in Machine Learning Capabilities
Yes (BigQuery ML with 15+ model types)
—
Cloud Provider Support(providers)
1 (Google Cloud only)
—
SQL Compatibility(percentage)
ANSI SQL-2011 standard
—
Built-in ML Capabilities
Yes (BigQuery ML)
—
Multi-Language Support(languages)
SQL, Python, Scala, R, Java
—
Supported Cloud Platforms(count)
AWS, Azure, GCP
—
Setup Time to Production(minutes)
1-2 weeks
—
Initial Setup Time to Production(weeks)
1-2 weeks
—
On-Premises Deployment Option
No (cloud-only)
—
Setup Time to First Query(minutes)
5-10 minutes
—
Pre-built AutoML Models(models)
12+ model families via AutoML
—
Real-Time Notebook Collaboration Users(concurrent users)
Unlimited simultaneous editing
—
Users Per Collaborative Project(concurrent users)
Unlimited with real-time sync
—
Collaborative Notebooks with Version Control(native support)
Yes (built-in with Git integration)
—
Native AWS Service Integrations(services)
15+ (S3, RDS, Kinesis)
—
BI Tool Native Connectors(count)
65
—
Delta Lake Support
Native Delta Lake engine
—
Training Job Spot Instance Discount(%)
Up to 70% savings
—
Initial Licensing Cost(USD)
$2,000-$15,000/month
—
Annual Cost (100TB/year, 5-node baseline)(USD thousands)
$120,000-$180,000
—
Total Cost of Ownership (Annual, 100TB dataset)(USD)
$78,000-$156,000
—
Cluster Management Required(hours/month)
Minimal (<5 hours/month)
—
Infrastructure Management Required(null)
Manual cluster setup and scaling
Fully serverless, automatic scaling
Required Team Skills (FTE equivalents for operations)(FTE)
0.25 (minimal management)
—
Cluster Auto-scaling Capability(supported)
Automatic (5-30 min provisioning)
—
Infrastructure Management Overhead(hours per month)
0-5 hours
—
Built-in Security Features
6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation)
—
Supported Data Formats(formats)
All Spark formats + native Delta Lake optimization
—
Community Size(active users)
8,000+ questions
—
SQL Standard Compliance Level(null)
ANSI SQL with Spark extensions
—
Supported Data Connectors(count)
15+ native connectors
—
Enterprise SLA Uptime(percent)
99.9%
—
Enterprise SLA Availability(percent)
99.99%
—
Native ML/AI Features(null)
MLflow, Feature Store, AutoML included
—
ML Frameworks Supported(count)
8 frameworks (via MLflow ecosystem)
—
Data Consolidation Required(null)
Yes, into Delta Lake
—
Time to Deploy (Basic Setup)(days)
3-7 days
—
Typical Enterprise Migration Time(months)
3-6 months (focused data/AI projects)
—
Initial Deployment Time(minutes)
0.25 weeks (15 minutes)
—
Organizations Using Platform(count (thousands))
30,000+
—
Fortune 500 Adoption(%)
40%
—
Native ML Pipeline Integration(rating)
MLflow + Databricks Intelligence Engine (built-in)
—
Data Lakehouse ACID Support(capability)
Native Delta Lake with ACID, time travel, schema evolution
—
Data Governance Features(key capabilities)
Unity Catalog, lineage, access control, Delta Lake
—
Enterprise Customers(millions)
10,000+
—
Annual Customer Growth Rate (2025)(percent)
18% YoY
—
ML Feature Store(null)
Native MLflow Feature Store included
—
Native ML Framework Support
MLflow, Spark MLlib, TensorFlow, PyTorch
BigQuery ML (SQL-based only)
Native ML Ops Tools(tools included)
MLflow, Feature Store, Model Registry
—
Data Governance (Unity Catalog equivalent)(null)
Unity Catalog with lineage, tags, access control
—
Native Row/Column-Level Access Control(supported)
Yes (Unity Catalog native)
—
Setup Complexity (1=Simple, 10=Complex)(scale)
7/10
—
Setup Complexity (1-10 scale)(difficulty score)
7
—
Supported Data Types(types)
Structured, semi-structured, unstructured
Structured (relational tables)
Cloud Providers(count)
3 (AWS, Azure, GCP)
1 (Google Cloud only)
Global Region Availability(regions)
60+ (via partner clouds)
—
Supported Cloud Providers(number of platforms)
1 (GCP only)
—
Minimum Learning Curve (months for competency)(months)
2-3 months
2-4 weeks
Data Warehouse Setup Time(minutes)
15-30 minutes
—
Setup Time to First Query(minutes)
30-60 minutes (cluster creation)
5-10 minutes (serverless)
Initial Setup Time(minutes)
0.25 days
—
Community Size (GitHub stars)(stars)
8,200 stars (databricks/databricks-cli)
—
Data Governance Granularity(access level)
Column, row, and table-level with tags
—
Enterprise Service Count(services)
50+ (data/AI focused)
—
ACID Transaction Support(boolean)
Native (Delta Lake)
No (append-only snapshots)
Concurrent Users Support(users)
Unlimited (serverless SQL analytics)
—
Maximum Supported Dataset Size(TB)
1000+ TB (unlimited)
—
Concurrent User Queries(users)
1000+ simultaneous
—
Max Concurrent Queries (single cluster)(queries)
Unlimited (auto-scaling)
—
ML Model Training Cost Efficiency(relative cost index)
1.0x baseline (integrated ML platform)
—
Data Format Lock-in Risk
Low (open Delta/Iceberg formats)
—
Data Marketplace Size(number of datasets)
~200 datasets
—
GitHub Stars (Community Traction)(thousands)
N/A (Google proprietary)
—
Maximum Ingestion Rate(events/second)
1,000,000
—
Data Compression Ratio(x compression)
5:1 average
—
Support for Time-Series Data
Supported but not optimized
—
Pros & Cons
10 pros·6 cons across both
B
Databricks
+5-3
Pros
- Native Delta Lake format with ACID transactions and time-travel capabilities
- Unified workspace for SQL, Python, Scala, R, and notebooks with collaborative features
- Built-in MLflow and Feature Store for end-to-end ML lifecycle management
- Flexible compute scaling with auto-termination and cluster management
- Multi-cloud deployment (AWS, Azure, GCP) with consistent experience
Cons
- Requires manual cluster configuration and compute cost optimization expertise
- Steeper learning curve for SQL-only analysts compared to traditional data warehouses
- Infrastructure management overhead increases operational complexity
B
BigQuery
+5-3
Pros
- Fully serverless with zero infrastructure management—query in seconds after setup
- Industry-leading query performance with columnar storage and vectorization
- Seamless integration with Google Cloud ecosystem (Looker, Dataflow, Vertex AI)
- Transparent pay-per-query model ($6-8/TB scanned) with no idle costs
- Automatic scaling and query optimization without user intervention
Cons
- Limited multi-language support—primarily SQL-focused, weak Python/R integration
- Vendor lock-in to Google Cloud ecosystem with limited portability
- Less native ML/feature engineering tooling compared to Databricks
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more about our affiliate disclosure
Wikipedia
- W
Databricks on Wikipedia (opens in new tab)
Unified analytics platform built on Apache Spark with lakehouse architecture for data engineering, analytics, and ML.
- W
BigQuery on Wikipedia (opens in new tab)
Google's fully managed, serverless data warehouse with columnar storage optimized for SQL analytics at scale.
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