software
Snowflake vs Databricks 2026: Data Warehouse vs Lakehouse
Snowflake is a pure cloud data warehouse optimized for SQL analytics with consumption-based pricing, while Databricks is a unified lakehouse platform combining data warehousing and AI/ML workloads with Apache Spark at its core. Databricks excels for organizations needing integrated machine learning, while Snowflake dominates traditional analytics and BI use cases.
Snowflake
Enterprise cloud data warehouse with native multi-cloud support and broad BI ecosystem integration.
Organizations focused on BI, reporting, data warehousing, and SQL analytics; enterprises prioritizing simplicity and query performance over ML integration.
Score63%
VS
Databricks
AI and data lakehouse platform built on Apache Spark for unified analytics and ML workflows
Data-driven organizations needing integrated analytics and ML, enterprises with diverse data formats, teams requiring distributed processing, or companies building AI/ML features.
Score63%
216 attributes7 differences16 pros/cons
Quick Answer
AI SummarySnowflake is a pure cloud data warehouse optimized for SQL analytics with consumption-based pricing, while Databricks is a unified lakehouse platform combining data warehousing and AI/ML workloads with Apache Spark at its core. Databricks excels for organizations needing integrated machine learning, while Snowflake dominates traditional analytics and BI use cases.
Our Verdict
AI-assistedChoose Snowflake if your primary focus is SQL-based analytics, business intelligence, and cost-predictability with straightforward data warehousing needs. Choose Databricks if you need a unified platform for analytics AND machine learning, work with diverse data formats, or require Apache Spark's distributed processing capabilities for complex transformations.
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Snowflake
8.3/10
Databricks
6.7/10
Choose Snowflake if
Best pickOrganizations focused on BI, reporting, data warehousing, and SQL analytics; enterprises prioritizing simplicity and query performance over ML integration.
Choose Databricks if
Data-driven organizations needing integrated analytics and ML, enterprises with diverse data formats, teams requiring distributed processing, or companies building AI/ML features.
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Key Differences at a Glance
- Core Architecture:✓ Databricks wins(Lakehouse (Delta Lake on object storage) vs Cloud data warehouse (columnar storage))
- ML/AI Capabilities:✓ Databricks wins(Native (Databricks ML, MLflow built-in) vs Limited (requires external tools))
- Query Engine:Proprietary SQL engine vs Apache Spark (distributed processing)
Key Facts & Figures
150 numeric metrics compared
| Metric | Snowflake | Databricks | Ratio |
|---|---|---|---|
| Starting Monthly Cost(USD) | $2,000-$5,000 | $1,500-$4,000 | |
| Setup Time(minutes) | 1-3 days | 3-7 days | |
| Query Performance (TPC-DS)(seconds) | 15-20 | 18-25 | |
| ML/AI Integration Score(out of 10) | 4/10 | 9/10 | |
| Global Enterprise Customers(count (2026)) | 10,000+ | 6,500+ | |
| Supported Cloud Providers(number of platforms) | 3 (AWS, Azure, GCP) | — | — |
| Setup Time to First Query(minutes) | 20-30 minutes | — | — |
| Data Marketplace Size(number of datasets) | 1,000+ datasets | — | — |
| Annual Customer Growth Rate (2025)(percent) | 22% YoY | — | — |
| Average Enterprise Contract Value(USD thousands per year) | $200-500 | — | — |
| Base Cost per TB (Monthly)(USD) | $4-6 | — | — |
| Available Cloud Providers(count) | AWS, Azure, GCP | — | — |
| Average Query Response Time(seconds) | 2-4 seconds | — | — |
| Time to Production (median)(weeks) | 1-3 weeks | — | — |
| Market Share 2026(percent) | 32% | — | — |
| Query Latency (1 billion rows)(seconds) | 30 seconds | — | — |
| Monthly Cost (100 GB compressed)(USD) | $1,500 | — | — |
| Ingestion Throughput(events/sec) | 100,000 events/sec | — | — |
| Data Retention for Time-Travel(days) | 90 days | — | — |
| Compression Ratio(ratio) | 4:1 to 8:1 | — | — |
| Learning Curve (1-10 scale)(difficulty) | 3/10 (very easy) | — | — |
| Data Warehouse Query Speed (Typical)(seconds) | <5 seconds | — | — |
| Query Latency (1TB dataset)(seconds) | 30-120 seconds | — | — |
| Deployment Time(seconds) | 0.3-0.5 weeks (1-2 days) | — | — |
| Annual Cost (100TB storage, 10 users)(USD) | $120,000-180,000 | — | — |
| Maximum Scalability(concurrent container instances) | Up to 50+ PB (cloud limits) | — | — |
| Time to First Query (production)(days) | 1-3 days | — | — |
| Required Technical Expertise Level(years experience needed) | 1-2 years (SQL knowledge) | — | — |
| Annual License Cost (100TB data)(USD) | $240,000 | — | — |
| Uptime SLA Guarantee(percent) | 99.99% | — | — |
| Query Response Time (10TB scan)(seconds) | 8.2 | — | — |
| Data Format Support Count(formats) | 8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images) | — | — |
| Available Integrations(integrations) | 600+ | — | — |
| Time to Production(days) | 0.5 | — | — |
| Query Latency (Typical)(milliseconds) | 1,000-10,000ms | — | — |
| SQL Standard Compliance(% compatibility) | 95% (full ANSI) | — | — |
| Enterprise Customers (2025)(count) | ~10,000 enterprises | — | — |
| Base Setup Cost (Annual)(USD) | $10,000-1,000,000 (credits-based) | — | — |
| Time to Insight (Complex Query)(seconds) | 3-15 (depends on data size) | — | — |
| Maximum Daily Data Volume(terabytes) | Unlimited (petabyte-scale) | — | — |
| Operational Complexity (1-10 scale)(complexity score) | 3/10 (managed cloud service) | — | — |
| SQL Query Performance (1TB dataset)(seconds) | 2-5 seconds | 8-15 seconds | |
| Base Monthly Cost (minimum)(USD) | $120-240 | $500+ | |
| Data Format Support | Structured (optimized for tables/CSV/JSON) | Delta, Parquet, ORC, CSV, JSON (open-source native) | — |
| Concurrent Users Support(users) | Unlimited (multi-cluster shared warehouse) | Unlimited (serverless SQL analytics) | |
| Data Warehouse Setup Time(minutes) | 5-10 minutes | 15-30 minutes | |
| Global Market Share (2024)(%) | 32% of cloud data warehouse market | 18% of lakehouse market | |
| ML Model Training Cost Efficiency(relative cost index) | 2.8x baseline (external ML tools required) | 1.0x baseline (integrated ML platform) | |
| Initial Setup Time(minutes) | 0.1 weeks (24 hours) | — | — |
| TPC-DS 100TB Query Performance(seconds) | 38 seconds | — | — |
| Base Hourly Cost (2-node cluster)(USD/hour) | $4.00-$6.00 (Medium warehouse) | — | — |
| Storage Cost (per TB/month)(USD) | $23 (on-demand) | — | — |
| Query Performance (TPC-DS 100GB)(seconds) | ~14 seconds | — | — |
| Scaling Adjustment Time(minutes) | ~1 (auto-scaling, no downtime) | — | — |
| Maximum Single Query Data Scanned(petabytes) | 20+ | — | — |
| Cloud Providers Supported(count) | 3 (AWS, Azure, GCP) | — | — |
| Annual Contract Discount(percent) | Up to 20% | — | — |
| Configuration Tuning Required(hours (estimated)) | 4-8 (clustering hints optional) | — | — |
| Starting Monthly Cost (10GB active data)(USD) | $480 | $650 | |
| SQL Query Performance (TPC-DS Benchmark)(seconds) | 28 | 45 | |
| BI Tool Native Connectors(count) | 150+ | 65 | |
| Maximum Concurrent Queries Per Warehouse(queries) | 8-128 (warehouse-dependent) | Unlimited (Spark clusters) | — |
| Customer Satisfaction Rating (G2 2025)(percent) | 85% | 82% | |
| Setup Complexity (1-10 scale)(difficulty score) | 4 | 7 | |
| Annual TCO (100TB storage, average usage)(USD) | $260,000 | — | — |
| TPC-DS Query Benchmark (100GB dataset)(seconds) | 38 | — | — |
| Setup Time to Production(minutes) | 10-15 hours | 1-2 weeks | |
| Data Marketplace Size(datasets) | 1,500+ | — | — |
| Reserved Instance Discount(percent) | None (on-demand only) | — | — |
| Query Latency (P99 percentile)(milliseconds) | 2500ms | — | — |
| Maximum Ingestion Rate(events/second) | 500,000 | — | — |
| Storage Cost(USD per TB per month) | $50 | — | — |
| Concurrent Query Capacity(concurrent users) | 1000+ | — | — |
| Time to First Query(minutes) | 5 (account creation) | — | — |
| Minimum Cluster Size(nodes) | 1 (virtual warehouse) | — | — |
| Query Performance (10TB TPC-DS benchmark)(seconds) | 5 seconds | — | — |
| Annual Cost (100TB, 24/7 usage)(USD) | $200,000 | — | — |
| Data Recovery (Time Travel)(days) | 90 days automatic | — | — |
| Required DevOps Team Size(FTE) | 0.5 engineers | — | — |
| Community Size (GitHub Stars)(stars) | 2,800 stars | 8,200 stars (databricks/databricks-cli) | |
| SQL Query Speed (relative benchmark)(relative to baseline) | Industry leading (100% baseline) | 2-5x speedup with Photon | |
| Minimum Setup Time(minutes) | 5-10 minutes (serverless) | 30-45 minutes (cluster setup) | |
| ML/AI Feature Maturity(1-10 scale) | 4/10 (limited native, requires integrations) | 9/10 (native MLflow, AutoML, Feature Store) | |
| Base Compute Cost (per hour)(USD) | $2.00-$4.00 per credit | $0.40-$1.20 per DBU | |
| Enterprise Customer Adoption(% of market) | 32% enterprise market share (2025) | 18% enterprise market share (2025) | |
| ETL/ELT Performance (1TB dataset)(minutes) | 20-35 minutes (SQL based) | 8-15 minutes (Spark optimized) | |
| Time to Production Deployment(days) | 2-3 days | — | — |
| Estimated Annual Cost (1 PB throughput)(USD) | $280,000-$360,000 | — | — |
| Cloud Provider Support(count) | 3 (AWS, Azure, GCP) | — | — |
| Median Ad-hoc Query Response Time(seconds) | 3-5 seconds | — | — |
| Concurrent Users per Instance(users) | 500-1000+ elastic | — | — |
| Data Compression Ratio(ratio) | 3-5:1 average | — | — |
| Minimum Compute Billing Unit(seconds) | Per-second (1 credit minimum per query) | — | — |
| Minimum Annual Cost(USD) | $4,000-8,000 | — | — |
| Native BI Tool Connectors(count) | 30+ (Tableau, Power BI, Looker, Qlik, Sisense) | — | — |
| Uptime SLA(percent) | 99.9% | — | — |
| Time-Travel Query Window(days) | 90 days retention | — | — |
| Starting Compute Cost (per hour)(USD) | $0.30 (1 DBU compute) | $0.30 (1 DBU compute) | |
| Pre-built AutoML Models(models) | 12+ model families via AutoML | 12+ model families via AutoML | |
| Native AWS Service Integrations(services) | 15+ (S3, RDS, Kinesis) | 15+ (S3, RDS, Kinesis) | |
| Training Job Spot Instance Discount(%) | Up to 70% savings | Up to 70% savings | |
| SQL Query Performance (sample 1TB table)(seconds) | 8-15 (native optimizations) | 8-15 (native optimizations) | |
| Starting Monthly Cost (Small Team)(USD) | $500-2,000 | $500-2,000 | |
| Supported Data Connectors(count) | 15+ native connectors | 15+ native connectors | |
| Enterprise SLA Uptime(percent) | 99.9% | 99.9% | |
| Average Query Latency (Analytical)(seconds) | 1-5 seconds (on cached data) | 1-5 seconds (on cached data) | |
| Time to Deploy (Basic Setup)(days) | 3-7 days | 3-7 days | |
| Monthly Starting Cost(USD) | $600-900 | $600-900 | |
| Apache Spark Query Performance Boost(x faster vs open-source) | 10x (Photon engine) | 10x (Photon engine) | |
| Available Services(count) | 25+ integrated | 25+ integrated | |
| BigQuery/Equivalent Query Speed (1TB dataset)(seconds) | 15-30 sec (via Databricks SQL) | 15-30 sec (via Databricks SQL) | |
| Organizations Using Platform(count (thousands)) | 30,000+ | 30,000+ | |
| Enterprise Customers(millions) | 10,000+ | 10,000+ | |
| Query Latency (Average)(milliseconds) | 40-100 ms | 40-100 ms | |
| Compute Cost Per Hour(USD) | $0.40-0.50 | $0.40-0.50 | |
| Setup Complexity (1=Simple, 10=Complex)(scale) | 7/10 | 7/10 | |
| Typical Query Latency (Structured Data)(seconds) | 5-15 seconds | 5-15 seconds | |
| Cloud Providers(count) | 3 (AWS, Azure, GCP) | 3 (AWS, Azure, GCP) | |
| Minimum Learning Curve (months for competency)(months) | 2-3 months | 2-3 months | |
| Starting Monthly Cost (1 TB storage + compute)(USD) | $400-800 (variable compute) | $400-800 (variable compute) | |
| Spark Performance (Query Speed)(x faster relative to standard Spark) | 10-100x faster (Photon engine) | 10-100x faster (Photon engine) | |
| Total Service Offerings(services) | ~15 core data/AI services | ~15 core data/AI services | |
| Compute Instance Cost (Standard)(USD per hour) | $0.50-$2.50 (depends on cloud provider) | $0.50-$2.50 (depends on cloud provider) | |
| Typical Enterprise Migration Time(months) | 3-6 months (focused data/AI projects) | 3-6 months (focused data/AI projects) | |
| Initial Setup Time to Production(weeks) | 1-2 weeks | 1-2 weeks | |
| Processing Speed vs MapReduce Baseline(times faster) | 10-100x faster | 10-100x faster | |
| Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) | $550-850 | $550-850 | |
| Required Team Skills (FTE equivalents for operations)(FTE) | 0.25 (minimal management) | 0.25 (minimal management) | |
| SQL Query Standards Compliance(% ANSI SQL support) | Full ANSI SQL (100%) | Full ANSI SQL (100%) | |
| Query Latency (median, standard ETL workload)(seconds) | 3.5-8 seconds | 3.5-8 seconds | |
| Built-in Collaboration Tools (notebooks, dashboards, repos)(count) | Notebooks, Dashboards, SQL Editor, Repos, MLflow | Notebooks, Dashboards, SQL Editor, Repos, MLflow | |
| SQL Query Performance (TPC-DS 100TB)(seconds) | 285 seconds | 285 seconds | |
| Spark Job Acceleration(multiplier) | 3-5x faster (Photon engine) | 3-5x faster (Photon engine) | |
| ML Frameworks Supported(count) | 8 frameworks (via MLflow ecosystem) | 8 frameworks (via MLflow ecosystem) | |
| Global Region Availability(regions) | 60+ (via partner clouds) | 60+ (via partner clouds) | |
| Enterprise Service Count(services) | 50+ (data/AI focused) | 50+ (data/AI focused) | |
| Starting Monthly Cost (10TB workload)(USD) | $3,500-$5,000 | $3,500-$5,000 | |
| Initial Deployment Time(weeks) | 0.25 weeks (15 minutes) | 0.25 weeks (15 minutes) | |
| Processing Speed (Iterative ML)(x relative to baseline) | 50-100x faster (Spark + Photon) | 50-100x faster (Spark + Photon) | |
| SQL Query Latency (100GB dataset)(seconds) | 0.5-3 seconds (Photon) | 0.5-3 seconds (Photon) | |
| Annual Cost (100TB/year, 5-node baseline)(USD thousands) | $120,000-$180,000 | $120,000-$180,000 | |
| Query Latency (1TB dataset scan)(seconds) | 8-12 seconds | 8-12 seconds | |
| Setup Time to First Query(minutes) | 30-60 minutes (cluster creation) | 30-60 minutes (cluster creation) | |
| Compute Cost (1000 compute hours/month)(USD) | $2,400-3,600 (on-demand, m5.large cluster) | $2,400-3,600 (on-demand, m5.large cluster) | |
| Storage Cost per TB/month(USD) | $0.03-0.05/GB ($30-50/TB) | $0.03-0.05/GB ($30-50/TB) | |
| Maximum Query Timeout(hours) | 24 hours (configurable) | 24 hours (configurable) | |
| Starting Price (Monthly)(USD) | $600 (for 2 DBUs minimum) | $600 (for 2 DBUs minimum) | |
| Total Services Available(count) | 15 integrated services | 15 integrated services | |
| Apache Spark Performance Improvement(multiple) | 10-100x faster with Photon engine | 10-100x faster with Photon engine | |
| ML Model Frameworks Supported(frameworks) | 10+ (via Mosaic AI and MLflow) | 10+ (via Mosaic AI and MLflow) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Evenly matched1 tie
Snowflake
Attribute
Databricks
- Cloud data warehouse (columnar storage)Core ArchitectureLakehouse (Delta Lake on object storage)(winner)
- Limited (requires external tools)ML/AI CapabilitiesNative (Databricks ML, MLflow built-in)(winner)
- Proprietary SQL engineQuery EngineApache Spark (distributed processing)
- $2-4 per credit (~$120-240 minimum)(winner)Starting Price (monthly)$0.30-0.55 per DBU (~$500+ minimum)
- Superior (optimized for BI/analytics)(winner)SQL Analytics PerformanceCompetitive (good for SQL, better for mixed workloads)
- Structured data (requires ingestion)Data Format FlexibilityAny format (raw data, structured, unstructured)(winner)
- Faster (pre-optimized queries, ~2-5 seconds)(winner)Time-to-Insight for AnalyticsModerate (Spark optimization, ~5-15 seconds typical)
- Core Architecture
Snowflake
Cloud data warehouse (columnar storage)
Databricks
Lakehouse (Delta Lake on object storage)(winner)
- ML/AI Capabilities
Snowflake
Limited (requires external tools)
Databricks
Native (Databricks ML, MLflow built-in)(winner)
- Query Engine
Snowflake
Proprietary SQL engine
Databricks
Apache Spark (distributed processing)
- Starting Price (monthly)
Snowflake
$2-4 per credit (~$120-240 minimum)(winner)
Databricks
$0.30-0.55 per DBU (~$500+ minimum)
- SQL Analytics Performance
Snowflake
Superior (optimized for BI/analytics)(winner)
Databricks
Competitive (good for SQL, better for mixed workloads)
- Data Format Flexibility
Snowflake
Structured data (requires ingestion)
Databricks
Any format (raw data, structured, unstructured)(winner)
- Time-to-Insight for Analytics
Snowflake
Faster (pre-optimized queries, ~2-5 seconds)(winner)
Databricks
Moderate (Spark optimization, ~5-15 seconds typical)
Full Comparison
| Attribute | Snowflake | Databricks |
|---|---|---|
| Starting Monthly Cost(USD) | $2,000-$5,000 | $1,500-$4,000(winner) |
| Base Query Cost(USD per TB scanned) | $2-4 per credit | — |
| Average Enterprise Contract Value(USD thousands per year) | $200-500 | — |
| Base Cost per TB (Monthly)(USD) | $4-6 | — |
| Monthly Cost (100 GB compressed)(USD) | $1,500 | — |
Show 22 more attributesAnnual License Cost (100TB data)(USD) $240,000 — Base Setup Cost (Annual)(USD) $10,000-1,000,000 (credits-based) — Base Monthly Cost (minimum)(USD) $120-240 $500+ Base Hourly Cost (2-node cluster)(USD/hour) $4.00-$6.00 (Medium warehouse) — Annual Contract Discount(percent) Up to 20% — Starting Monthly Cost (10GB active data)(USD) $480 $650 Reserved Instance Discount(percent) None (on-demand only) — Storage Cost(USD per TB per month) $50 — Base Compute Cost (per hour)(USD) $2.00-$4.00 per credit $0.40-$1.20 per DBU Minimum Annual Cost(USD) $4,000-8,000 — Per-Query Compute Cost Model(structure) $2-4 per credit (1 credit = compute + storage) — 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 — Starting Monthly Cost (1 TB storage + compute)(USD) $400-800 (variable compute) — 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 — Compute Cost (1000 compute hours/month)(USD) $2,400-3,600 (on-demand, m5.large cluster) — Storage Cost per TB/month(USD) $0.03-0.05/GB ($30-50/TB) — Starting Price (Monthly)(USD) $600 (for 2 DBUs minimum) — | ||
| Setup Time(minutes) | 1-3 days(winner) | 3-7 days |
| Initial Setup Time(minutes) | 0.1 weeks (24 hours) | — |
| Customer Satisfaction Rating (G2 2025)(percent) | 85%(winner) | 82% |
| Query Performance (TPC-DS)(seconds) | 15-20(winner) | 18-25 |
| Maximum Query Timeout(hours) | Limited by warehouse size | — |
| Concurrent User Support(scalability level) | Limited by warehouse size, manual tuning | — |
| Average Query Response Time(seconds) | 2-4 seconds | — |
| Query Latency (1 billion rows)(seconds) | 30 seconds | — |
Show 37 more attributesIngestion Throughput(events/sec) 100,000 events/sec — Data Warehouse Query Speed (Typical)(seconds) <5 seconds — Query Latency (1TB dataset)(seconds) 30-120 seconds — Deployment Time(seconds) 0.3-0.5 weeks (1-2 days) — Maximum Scalability(concurrent container instances) Up to 50+ PB (cloud limits) — Query Response Time (10TB scan)(seconds) 8.2 — Query Latency (Typical)(milliseconds) 1,000-10,000ms — Data Ingestion Rate(GB/sec) Batch-based (bulk loading) — Time to Insight (Complex Query)(seconds) 3-15 (depends on data size) — SQL Query Performance (1TB dataset)(seconds) 2-5 seconds 8-15 seconds TPC-DS 100TB Query Performance(seconds) 38 seconds — Query Performance (TPC-DS 100GB)(seconds) ~14 seconds — SQL Query Performance (TPC-DS Benchmark)(seconds) 28 45 Maximum Concurrent Queries Per Warehouse(queries) 8-128 (warehouse-dependent) Unlimited (Spark clusters) TPC-DS Query Benchmark (100GB dataset)(seconds) 38 — Query Latency (P99 percentile)(milliseconds) 2500ms — Query Performance (10TB TPC-DS benchmark)(seconds) 5 seconds — SQL Query Speed (relative benchmark)(relative to baseline) Industry leading (100% baseline) 2-5x speedup with Photon ETL/ELT Performance (1TB dataset)(minutes) 20-35 minutes (SQL based) 8-15 minutes (Spark optimized) Median Ad-hoc Query Response Time(seconds) 3-5 seconds — Query Performance on Data Lakes(relative speed) Slower without ingestion; 5-10x slower on unoptimized data lake queries — 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) — Query Latency (Average)(milliseconds) 40-100 ms — Typical Query Latency (Structured Data)(seconds) 5-15 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) — 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 — Maximum Query Timeout(hours) 24 hours (configurable) — Apache Spark Performance Improvement(multiple) 10-100x faster with Photon engine — | ||
| ML/AI Integration Score(out of 10) | 4/10 | 9/10(winner) |
| Native ML Framework Integration | Cortex AI (basic) | MLflow + Spark ML |
| ML/AI Feature Maturity(1-10 scale) | 4/10 (limited native, requires integrations) | 9/10 (native MLflow, AutoML, Feature Store)(winner) |
| Available Services(count) | 25+ integrated | — |
| Global Enterprise Customers(count (2026)) | 10,000+(winner) | 6,500+ |
| Market Share 2026(percent) | 32% | — |
| Global Market Share (2024)(%) | 32% of cloud data warehouse market(winner) | 18% of lakehouse market |
| Enterprise Customer Adoption(% of market) | 32% enterprise market share (2025)(winner) | 18% enterprise market share (2025) |
| Supported Data Formats(types) | Structured (Parquet, CSV, JSON) | All formats (Delta, Parquet, Images, Videos, Audio) |
| Data Sharing Standard(technology) | Snowflake Marketplace (proprietary) | Delta Sharing (open standard) |
| Data Sharing Capability | Native, cross-account/cross-cloud | — |
| Zero-Copy Cloning | Available (instant, free) | — |
| Data Retention for Time-Travel(days) | 90 days | — |
| Data Format Support Count(formats) | 8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images) | — |
Show 9 more attributesNative ML/AI Capabilities Limited (external integration required) Native (MLflow, AutoML, Feature Store) Data Format Support Structured (optimized for tables/CSV/JSON) Delta, Parquet, ORC, CSV, JSON (open-source native) Data Recovery (Time Travel)(days) 90 days automatic — Native Multi-Cloud Data Sharing(boolean) Yes (zero-copy) — Time-Travel Query Window(days) 90 days retention — Total Service Offerings(services) ~15 core data/AI services — SQL Query Standards Compliance(% ANSI SQL support) Full ANSI SQL (100%) — Built-in Collaboration Tools (notebooks, dashboards, repos)(count) Notebooks, Dashboards, SQL Editor, Repos, MLflow — MLOps/ML Tooling Native MLflow, Feature Store, AutoML, Model Registry — | ||
| Multi-Language Support(languages) | SQL primarily | SQL, Python, Scala, R, Java |
| Supported Cloud Providers(number of platforms) | 3 (AWS, Azure, GCP) | — |
| Available Cloud Providers(count) | AWS, Azure, GCP | — |
| Deployment Options | Cloud-only (SaaS) | Cloud-only (3 regions) |
| Cloud Platform Support | AWS, Azure, GCP | — |
| Cloud Provider Support(count) | 3 (AWS, Azure, GCP) | — |
Show 3 more attributesCloud Providers(count) 3 (AWS, Azure, GCP) — Global Region Availability(regions) 60+ (via partner clouds) — Multi-Cloud Support(clouds supported) 3 clouds (AWS, Azure, GCP) — | ||
| Setup Time to First Query(minutes) | 20-30 minutes | — |
| Time to Production(days) | 0.5 | — |
| Setup Time to Production(minutes) | 10-15 hours | 1-2 weeks(winner) |
| Supported Cloud Platforms(count) | AWS, Azure, GCP | — |
| Initial Setup Time to Production(weeks) | 1-2 weeks | — |
Show 1 more attributeOn-Premises Deployment Option(availability) No (cloud-only) — | ||
| Data Marketplace Size(number of datasets) | 1,000+ datasets | — |
| Annual Customer Growth Rate (2025)(percent) | 22% YoY | — |
| Enterprise Customers(millions) | 10,000+ | — |
| Compute-Storage Decoupling | Complete separation | — |
| Compute-Storage Decoupling | Independent scaling | — |
| Compute & Storage Coupling | Fully independent (separate pricing) | — |
| Data Movement Required(percentage) | 100% (must ingest into Snowflake) | — |
| Data Lake S3/ADLS Support(native support) | No (requires data copy into Snowflake) | — |
Show 1 more attributeData Consolidation Required(null) Yes, into Delta Lake — | ||
| Time to Production (median)(weeks) | 1-3 weeks | — |
| Time to First Query (production)(days) | 1-3 days | — |
| Time to Deploy (Basic Setup)(days) | 3-7 days | — |
| Typical Enterprise Migration Time(months) | 3-6 months (focused data/AI projects) | — |
| Initial Deployment Time(weeks) | 0.25 weeks (15 minutes) | — |
| Setup Time(minutes) | 15 minutes | — |
| Minimum Setup Time(minutes) | 5-10 minutes (serverless)(winner) | 30-45 minutes (cluster setup) |
| Setup Time to First Query(minutes) | 30-60 minutes (cluster creation) | — |
| Compression Ratio(ratio) | 4:1 to 8:1 | — |
| Licensing Model | Consumption-based (compute + storage) | — |
| Learning Curve (1-10 scale)(difficulty) | 3/10 (very easy) | — |
| Setup Complexity (1=Simple, 10=Complex)(scale) | 7/10 | — |
| Annual Cost (100TB storage, 10 users)(USD) | $120,000-180,000 | — |
| Storage Cost (per TB/month)(USD) | $23 (on-demand) | — |
| Annual TCO (100TB storage, average usage)(USD) | $260,000 | — |
| Estimated Annual Cost (1 PB throughput)(USD) | $280,000-$360,000 | — |
| Initial Licensing Cost(USD) | $2,000-$15,000/month | — |
Show 1 more attributeAnnual Cost (100TB/year, 5-node baseline)(USD thousands) $120,000-$180,000 — | ||
| Supported Query Languages(count) | SQL, Python, Java, JavaScript, Scala | — |
| Required Technical Expertise Level(years experience needed) | 1-2 years (SQL knowledge) | — |
| Time to Production Deployment(days) | 2-3 days | — |
| Real-time Analytics Capability | Yes (sub-second latency) | — |
| SQL Query Support | ANSI SQL with advanced optimizations | — |
| Uptime SLA Guarantee(percent) | 99.99% | — |
| Uptime SLA(percent) | 99.9% | — |
| Enterprise SLA Uptime(percent) | 99.9% | — |
| Maximum Concurrent Users(users) | Unlimited | — |
| Maximum Daily Data Volume(terabytes) | Unlimited (petabyte-scale) | — |
| Concurrent Users Support(users) | Unlimited (multi-cluster shared warehouse) | Unlimited (serverless SQL analytics) |
| Concurrent Query Capacity(concurrent users) | 1000+ | — |
| Minimum Cluster Size(nodes) | 1 (virtual warehouse) | — |
Show 1 more attributeConcurrent Users per Instance(users) 500-1000+ elastic — | ||
| Available Integrations(integrations) | 600+ | — |
| Data Marketplace Size(datasets) | 1,500+ | — |
| SQL Standard Compliance(% compatibility) | 95% (full ANSI) | — |
| SQL Compliance | ANSI SQL compliant | — |
| Supported Data Formats(formats) | All Spark formats + native Delta Lake optimization | — |
| Supported Programming Languages(count) | SQL, Python, Scala, R, Java (full support) | — |
| Enterprise Customers (2025)(count) | ~10,000 enterprises | — |
| Operational Complexity (1-10 scale)(complexity score) | 3/10 (managed cloud service) | — |
| Data Warehouse Setup Time(minutes) | 5-10 minutes(winner) | 15-30 minutes |
| Setup Complexity (1-10 scale)(difficulty score) | 4(winner) | 7 |
| Minimum Learning Curve (months for competency)(months) | 2-3 months | — |
| ML Model Training Cost Efficiency(relative cost index) | 2.8x baseline (external ML tools required) | 1.0x baseline (integrated ML platform)(winner) |
| Scaling Adjustment Time(minutes) | ~1 (auto-scaling, no downtime) | — |
| Configuration Tuning Required(hours (estimated)) | 4-8 (clustering hints optional) | — |
| Required DevOps Team Size(FTE) | 0.5 engineers | — |
| Cluster Management Required(hours/month) | Minimal (<5 hours/month) | — |
| Infrastructure Management Required(null) | Manual cluster setup and scaling | — |
Show 2 more attributesRequired Team Skills (FTE equivalents for operations)(FTE) 0.25 (minimal management) — Cluster Auto-scaling Capability(supported) Automatic (5-30 min provisioning) — | ||
| Maximum Single Query Data Scanned(petabytes) | 20+ | — |
| Cloud Providers Supported(count) | 3 (AWS, Azure, GCP) | — |
| Supported Data Connectors(count) | 15+ native connectors | — |
| Microsoft Ecosystem Integration | Limited (Power BI via connector only) | — |
| Data Format Lock-in Risk | High (proprietary format) | Low (open Delta/Iceberg formats) |
| BI Tool Native Connectors(count) | 150+(winner) | 65 |
| Native BI Tool Connectors(count) | 30+ (Tableau, Power BI, Looker, Qlik, Sisense) | — |
| Native AWS Service Integrations(services) | 15+ (S3, RDS, Kinesis) | — |
| Maximum Ingestion Rate(events/second) | 500,000 | — |
| Time to First Query(minutes) | 5 (account creation) | — |
| Annual Cost (100TB, 24/7 usage)(USD) | $200,000 | — |
| Community Size (GitHub Stars)(stars) | 2,800 stars | 8,200 stars (databricks/databricks-cli)(winner) |
| Community Size(members) | 8,000+ questions | — |
| Unstructured Data Support(capability level) | Limited (structured tables primary) | Excellent (images, videos, text, PDFs) |
| Data Sharing Zero-Copy(capability level) | Native (Secure Shares, production-ready) | Partial (Delta Sharing, emerging) |
| 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) | — |
| Data Compression Ratio(ratio) | 3-5:1 average | — |
| Minimum Compute Billing Unit(seconds) | Per-second (1 credit minimum per query) | — |
| Pre-built AutoML Models(models) | 12+ model families via AutoML | — |
| Delta Lake Support | Native Delta Lake engine | — |
| Training Job Spot Instance Discount(%) | Up to 70% savings | — |
| Built-in Security Features | 6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation) | — |
| SQL Standard Compliance Level(null) | ANSI SQL with Spark extensions | — |
| Native ML/AI Features(null) | MLflow, Feature Store, AutoML included | — |
| ML Frameworks Supported(count) | 8 frameworks (via MLflow ecosystem) | — |
| Organizations Using Platform(count (thousands)) | 30,000+ | — |
| Native ML Pipeline Integration(rating) | MLflow + Databricks Intelligence Engine (built-in) | — |
| ML Model Frameworks Supported(frameworks) | 10+ (via Mosaic AI and MLflow) | — |
| 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 | — |
| ML Feature Store(null) | Native MLflow Feature Store included | — |
| Native ML Framework Support | MLflow, Spark MLlib, TensorFlow, PyTorch | — |
| 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) | — |
| Fortune 500 Adoption(%) | 40% | — |
| Supported Data Types(types) | Structured, semi-structured, unstructured | — |
| 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) | — |
| Total Services Available(count) | 15 integrated services | — |
| BigQuery Query Scale(Petabytes) | Not primary strength | — |
| Free Tier Data Processing Limit(GB/month) | 10 GB compute hours free | — |
| Unity Catalog Governance Features(features) | Data discovery, lineage, access control, PII detection | — |
SnowflakeDatabricksStarting Monthly Cost(USD)
$2,000-$5,000
$1,500-$4,000
Base Query Cost(USD per TB scanned)
$2-4 per credit
—
Average Enterprise Contract Value(USD thousands per year)
$200-500
—
Base Cost per TB (Monthly)(USD)
$4-6
—
Monthly Cost (100 GB compressed)(USD)
$1,500
—
Show 22 more attributes
Annual License Cost (100TB data)(USD)
$240,000
—
Base Setup Cost (Annual)(USD)
$10,000-1,000,000 (credits-based)
—
Base Monthly Cost (minimum)(USD)
$120-240
$500+
Base Hourly Cost (2-node cluster)(USD/hour)
$4.00-$6.00 (Medium warehouse)
—
Annual Contract Discount(percent)
Up to 20%
—
Starting Monthly Cost (10GB active data)(USD)
$480
$650
Reserved Instance Discount(percent)
None (on-demand only)
—
Storage Cost(USD per TB per month)
$50
—
Base Compute Cost (per hour)(USD)
$2.00-$4.00 per credit
$0.40-$1.20 per DBU
Minimum Annual Cost(USD)
$4,000-8,000
—
Per-Query Compute Cost Model(structure)
$2-4 per credit (1 credit = compute + storage)
—
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
—
Starting Monthly Cost (1 TB storage + compute)(USD)
$400-800 (variable compute)
—
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
—
Compute Cost (1000 compute hours/month)(USD)
$2,400-3,600 (on-demand, m5.large cluster)
—
Storage Cost per TB/month(USD)
$0.03-0.05/GB ($30-50/TB)
—
Starting Price (Monthly)(USD)
$600 (for 2 DBUs minimum)
—
Setup Time(minutes)
1-3 days
3-7 days
Initial Setup Time(minutes)
0.1 weeks (24 hours)
—
Customer Satisfaction Rating (G2 2025)(percent)
85%
82%
Query Performance (TPC-DS)(seconds)
15-20
18-25
Maximum Query Timeout(hours)
Limited by warehouse size
—
Concurrent User Support(scalability level)
Limited by warehouse size, manual tuning
—
Average Query Response Time(seconds)
2-4 seconds
—
Query Latency (1 billion rows)(seconds)
30 seconds
—
Show 37 more attributes
Ingestion Throughput(events/sec)
100,000 events/sec
—
Data Warehouse Query Speed (Typical)(seconds)
<5 seconds
—
Query Latency (1TB dataset)(seconds)
30-120 seconds
—
Deployment Time(seconds)
0.3-0.5 weeks (1-2 days)
—
Maximum Scalability(concurrent container instances)
Up to 50+ PB (cloud limits)
—
Query Response Time (10TB scan)(seconds)
8.2
—
Query Latency (Typical)(milliseconds)
1,000-10,000ms
—
Data Ingestion Rate(GB/sec)
Batch-based (bulk loading)
—
Time to Insight (Complex Query)(seconds)
3-15 (depends on data size)
—
SQL Query Performance (1TB dataset)(seconds)
2-5 seconds
8-15 seconds
TPC-DS 100TB Query Performance(seconds)
38 seconds
—
Query Performance (TPC-DS 100GB)(seconds)
~14 seconds
—
SQL Query Performance (TPC-DS Benchmark)(seconds)
28
45
Maximum Concurrent Queries Per Warehouse(queries)
8-128 (warehouse-dependent)
Unlimited (Spark clusters)
TPC-DS Query Benchmark (100GB dataset)(seconds)
38
—
Query Latency (P99 percentile)(milliseconds)
2500ms
—
Query Performance (10TB TPC-DS benchmark)(seconds)
5 seconds
—
SQL Query Speed (relative benchmark)(relative to baseline)
Industry leading (100% baseline)
2-5x speedup with Photon
ETL/ELT Performance (1TB dataset)(minutes)
20-35 minutes (SQL based)
8-15 minutes (Spark optimized)
Median Ad-hoc Query Response Time(seconds)
3-5 seconds
—
Query Performance on Data Lakes(relative speed)
Slower without ingestion; 5-10x slower on unoptimized data lake queries
—
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)
—
Query Latency (Average)(milliseconds)
40-100 ms
—
Typical Query Latency (Structured Data)(seconds)
5-15 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)
—
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
—
Maximum Query Timeout(hours)
24 hours (configurable)
—
Apache Spark Performance Improvement(multiple)
10-100x faster with Photon engine
—
ML/AI Integration Score(out of 10)
4/10
9/10
Native ML Framework Integration
Cortex AI (basic)
MLflow + Spark ML
ML/AI Feature Maturity(1-10 scale)
4/10 (limited native, requires integrations)
9/10 (native MLflow, AutoML, Feature Store)
Available Services(count)
25+ integrated
—
Global Enterprise Customers(count (2026))
10,000+
6,500+
Market Share 2026(percent)
32%
—
Global Market Share (2024)(%)
32% of cloud data warehouse market
18% of lakehouse market
Enterprise Customer Adoption(% of market)
32% enterprise market share (2025)
18% enterprise market share (2025)
Supported Data Formats(types)
Structured (Parquet, CSV, JSON)
All formats (Delta, Parquet, Images, Videos, Audio)
Data Sharing Standard(technology)
Snowflake Marketplace (proprietary)
Delta Sharing (open standard)
Data Sharing Capability
Native, cross-account/cross-cloud
—
Zero-Copy Cloning
Available (instant, free)
—
Data Retention for Time-Travel(days)
90 days
—
Data Format Support Count(formats)
8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images)
—
Show 9 more attributes
Native ML/AI Capabilities
Limited (external integration required)
Native (MLflow, AutoML, Feature Store)
Data Format Support
Structured (optimized for tables/CSV/JSON)
Delta, Parquet, ORC, CSV, JSON (open-source native)
Data Recovery (Time Travel)(days)
90 days automatic
—
Native Multi-Cloud Data Sharing(boolean)
Yes (zero-copy)
—
Time-Travel Query Window(days)
90 days retention
—
Total Service Offerings(services)
~15 core data/AI services
—
SQL Query Standards Compliance(% ANSI SQL support)
Full ANSI SQL (100%)
—
Built-in Collaboration Tools (notebooks, dashboards, repos)(count)
Notebooks, Dashboards, SQL Editor, Repos, MLflow
—
MLOps/ML Tooling
Native MLflow, Feature Store, AutoML, Model Registry
—
Multi-Language Support(languages)
SQL primarily
SQL, Python, Scala, R, Java
Supported Cloud Providers(number of platforms)
3 (AWS, Azure, GCP)
—
Available Cloud Providers(count)
AWS, Azure, GCP
—
Deployment Options
Cloud-only (SaaS)
Cloud-only (3 regions)
Cloud Platform Support
AWS, Azure, GCP
—
Cloud Provider Support(count)
3 (AWS, Azure, GCP)
—
Show 3 more attributes
Cloud Providers(count)
3 (AWS, Azure, GCP)
—
Global Region Availability(regions)
60+ (via partner clouds)
—
Multi-Cloud Support(clouds supported)
3 clouds (AWS, Azure, GCP)
—
Setup Time to First Query(minutes)
20-30 minutes
—
Time to Production(days)
0.5
—
Setup Time to Production(minutes)
10-15 hours
1-2 weeks
Supported Cloud Platforms(count)
AWS, Azure, GCP
—
Initial Setup Time to Production(weeks)
1-2 weeks
—
Show 1 more attribute
On-Premises Deployment Option(availability)
No (cloud-only)
—
Data Marketplace Size(number of datasets)
1,000+ datasets
—
Annual Customer Growth Rate (2025)(percent)
22% YoY
—
Enterprise Customers(millions)
10,000+
—
Compute-Storage Decoupling
Complete separation
—
Compute-Storage Decoupling
Independent scaling
—
Compute & Storage Coupling
Fully independent (separate pricing)
—
Data Movement Required(percentage)
100% (must ingest into Snowflake)
—
Data Lake S3/ADLS Support(native support)
No (requires data copy into Snowflake)
—
Show 1 more attribute
Data Consolidation Required(null)
Yes, into Delta Lake
—
Time to Production (median)(weeks)
1-3 weeks
—
Time to First Query (production)(days)
1-3 days
—
Time to Deploy (Basic Setup)(days)
3-7 days
—
Typical Enterprise Migration Time(months)
3-6 months (focused data/AI projects)
—
Initial Deployment Time(weeks)
0.25 weeks (15 minutes)
—
Setup Time(minutes)
15 minutes
—
Minimum Setup Time(minutes)
5-10 minutes (serverless)
30-45 minutes (cluster setup)
Setup Time to First Query(minutes)
30-60 minutes (cluster creation)
—
Compression Ratio(ratio)
4:1 to 8:1
—
Licensing Model
Consumption-based (compute + storage)
—
Learning Curve (1-10 scale)(difficulty)
3/10 (very easy)
—
Setup Complexity (1=Simple, 10=Complex)(scale)
7/10
—
Annual Cost (100TB storage, 10 users)(USD)
$120,000-180,000
—
Storage Cost (per TB/month)(USD)
$23 (on-demand)
—
Annual TCO (100TB storage, average usage)(USD)
$260,000
—
Estimated Annual Cost (1 PB throughput)(USD)
$280,000-$360,000
—
Initial Licensing Cost(USD)
$2,000-$15,000/month
—
Show 1 more attribute
Annual Cost (100TB/year, 5-node baseline)(USD thousands)
$120,000-$180,000
—
Supported Query Languages(count)
SQL, Python, Java, JavaScript, Scala
—
Required Technical Expertise Level(years experience needed)
1-2 years (SQL knowledge)
—
Time to Production Deployment(days)
2-3 days
—
Real-time Analytics Capability
Yes (sub-second latency)
—
SQL Query Support
ANSI SQL with advanced optimizations
—
Uptime SLA Guarantee(percent)
99.99%
—
Uptime SLA(percent)
99.9%
—
Enterprise SLA Uptime(percent)
99.9%
—
Maximum Concurrent Users(users)
Unlimited
—
Maximum Daily Data Volume(terabytes)
Unlimited (petabyte-scale)
—
Concurrent Users Support(users)
Unlimited (multi-cluster shared warehouse)
Unlimited (serverless SQL analytics)
Concurrent Query Capacity(concurrent users)
1000+
—
Minimum Cluster Size(nodes)
1 (virtual warehouse)
—
Show 1 more attribute
Concurrent Users per Instance(users)
500-1000+ elastic
—
Available Integrations(integrations)
600+
—
Data Marketplace Size(datasets)
1,500+
—
SQL Standard Compliance(% compatibility)
95% (full ANSI)
—
SQL Compliance
ANSI SQL compliant
—
Supported Data Formats(formats)
All Spark formats + native Delta Lake optimization
—
Supported Programming Languages(count)
SQL, Python, Scala, R, Java (full support)
—
Enterprise Customers (2025)(count)
~10,000 enterprises
—
Operational Complexity (1-10 scale)(complexity score)
3/10 (managed cloud service)
—
Data Warehouse Setup Time(minutes)
5-10 minutes
15-30 minutes
Setup Complexity (1-10 scale)(difficulty score)
4
7
Minimum Learning Curve (months for competency)(months)
2-3 months
—
ML Model Training Cost Efficiency(relative cost index)
2.8x baseline (external ML tools required)
1.0x baseline (integrated ML platform)
Scaling Adjustment Time(minutes)
~1 (auto-scaling, no downtime)
—
Configuration Tuning Required(hours (estimated))
4-8 (clustering hints optional)
—
Required DevOps Team Size(FTE)
0.5 engineers
—
Cluster Management Required(hours/month)
Minimal (<5 hours/month)
—
Infrastructure Management Required(null)
Manual cluster setup and scaling
—
Show 2 more attributes
Required Team Skills (FTE equivalents for operations)(FTE)
0.25 (minimal management)
—
Cluster Auto-scaling Capability(supported)
Automatic (5-30 min provisioning)
—
Maximum Single Query Data Scanned(petabytes)
20+
—
Cloud Providers Supported(count)
3 (AWS, Azure, GCP)
—
Supported Data Connectors(count)
15+ native connectors
—
Microsoft Ecosystem Integration
Limited (Power BI via connector only)
—
Data Format Lock-in Risk
High (proprietary format)
Low (open Delta/Iceberg formats)
BI Tool Native Connectors(count)
150+
65
Native BI Tool Connectors(count)
30+ (Tableau, Power BI, Looker, Qlik, Sisense)
—
Native AWS Service Integrations(services)
15+ (S3, RDS, Kinesis)
—
Maximum Ingestion Rate(events/second)
500,000
—
Time to First Query(minutes)
5 (account creation)
—
Annual Cost (100TB, 24/7 usage)(USD)
$200,000
—
Community Size (GitHub Stars)(stars)
2,800 stars
8,200 stars (databricks/databricks-cli)
Community Size(members)
8,000+ questions
—
Unstructured Data Support(capability level)
Limited (structured tables primary)
Excellent (images, videos, text, PDFs)
Data Sharing Zero-Copy(capability level)
Native (Secure Shares, production-ready)
Partial (Delta Sharing, emerging)
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)
—
Data Compression Ratio(ratio)
3-5:1 average
—
Minimum Compute Billing Unit(seconds)
Per-second (1 credit minimum per query)
—
Pre-built AutoML Models(models)
12+ model families via AutoML
—
Delta Lake Support
Native Delta Lake engine
—
Training Job Spot Instance Discount(%)
Up to 70% savings
—
Built-in Security Features
6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation)
—
SQL Standard Compliance Level(null)
ANSI SQL with Spark extensions
—
Native ML/AI Features(null)
MLflow, Feature Store, AutoML included
—
ML Frameworks Supported(count)
8 frameworks (via MLflow ecosystem)
—
Organizations Using Platform(count (thousands))
30,000+
—
Native ML Pipeline Integration(rating)
MLflow + Databricks Intelligence Engine (built-in)
—
ML Model Frameworks Supported(frameworks)
10+ (via Mosaic AI and MLflow)
—
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
—
ML Feature Store(null)
Native MLflow Feature Store included
—
Native ML Framework Support
MLflow, Spark MLlib, TensorFlow, PyTorch
—
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)
—
Fortune 500 Adoption(%)
40%
—
Supported Data Types(types)
Structured, semi-structured, unstructured
—
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)
—
Total Services Available(count)
15 integrated services
—
BigQuery Query Scale(Petabytes)
Not primary strength
—
Free Tier Data Processing Limit(GB/month)
10 GB compute hours free
—
Unity Catalog Governance Features(features)
Data discovery, lineage, access control, PII detection
—
Pros & Cons
10 pros·6 cons across both
Snowflake
+5-3
Pros
- Industry-leading SQL query performance with automatic optimization
- Zero-copy cloning and data sharing features for collaboration
- Simple, predictable per-credit consumption pricing model
- Native support for Iceberg and other open formats (as of 2024)
- Exceptional ease of use with minimal infrastructure management
Cons
- Significantly higher costs for ML/AI workloads compared to lakehouse alternatives
- Limited native ML/AI capabilities requiring external platforms (SageMaker, Databricks ML)
- Less flexible for unstructured data and semi-structured processing at scale
Databricks
+5-3
Pros
- Unified platform: analytics, ETL, streaming, and ML in one system
- Native MLflow for end-to-end ML lifecycle management and model tracking
- Superior data format flexibility (Delta Lake, Parquet, JSON, images, video)
- Better cost-efficiency for mixed workloads combining SQL and ML
- Serverless SQL analytics (Databricks SQL) with no cluster management
Cons
- Steeper learning curve due to Spark and distributed computing concepts
- SQL query performance slightly behind pure-warehouse solutions for standard analytics
- More complex pricing with compute, storage, and workspace tiers creating less predictability
Frequently Asked Questions
5 questions
Snowflake is typically cheaper for pure SQL analytics and BI use cases, with starting costs around $120-240/month. Databricks starts at $500+/month but becomes cost-competitive when combining analytics with ML/AI workloads, as you avoid paying for multiple separate platforms.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
Related Comparisons
12 more to explore
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Philo in 2026: Pricing, Lineup & How It Compares to Sling TV
As we head into 2026, Philo continues to position itself as an affordable streaming alternative for cable TV lovers. Discover what Philo offers, how its pricing stacks up against competitors like Sling TV, and what the Reddit community thinks about its future.
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