software
Hadoop vs Snowflake 2026: Cost & Performance
Hadoop is an open-source distributed computing framework for batch processing large datasets across clusters, while Snowflake is a cloud-native data warehouse optimized for SQL queries and real-time analytics. Snowflake offers faster query performance and easier management, while Hadoop provides greater flexibility and lower long-term costs for organizations with large data infrastructure investments.
AH
Apache Hadoop
Open-source distributed computing framework for batch processing large-scale data across clusters
Organizations with massive data volumes, on-premises infrastructure, strong technical teams, and batch processing workflows
Score63%
VS
Snowflake
Cloud-native SQL data warehouse with independent compute and storage scaling for fast analytics
Enterprises prioritizing speed and agility, teams without dedicated data infrastructure expertise, organizations needing real-time analytics and flexible scaling
Score63%
Quick Answer
AI SummaryHadoop is an open-source distributed computing framework for batch processing large datasets across clusters, while Snowflake is a cloud-native data warehouse optimized for SQL queries and real-time analytics. Snowflake offers faster query performance and easier management, while Hadoop provides greater flexibility and lower long-term costs for organizations with large data infrastructure investments.
Our Verdict
AI-assistedChoose Hadoop if you have massive unstructured data volumes, existing infrastructure investments, and technical teams capable of managing distributed systems—the lower long-term costs justify the complexity. Choose Snowflake if you prioritize fast time-to-insight, ease of management, real-time analytics, and are willing to pay cloud costs for elimination of DevOps overhead and superior query performance.
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A
Apache Hadoop
6.9/10
Snowflake
8.1/10
A
Choose Apache Hadoop if
Organizations with massive data volumes, on-premises infrastructure, strong technical teams, and batch processing workflows
Choose Snowflake if
Best pickEnterprises prioritizing speed and agility, teams without dedicated data infrastructure expertise, organizations needing real-time analytics and flexible scaling
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Key Differences at a Glance
- Architecture:✓ Snowflake wins(Cloud-native SQL data warehouse (MPP) vs Distributed computing framework (MapReduce-based))
- Query Speed (10TB dataset):✓ Snowflake wins(2-8 seconds vs 45-120 seconds)
- Infrastructure Cost (annual, 100TB):✓ Apache Hadoop wins($80,000-150,000 vs $150,000-250,000)
Key Facts & Figures
127 numeric metrics compared
| Metric | Apache Hadoop | Snowflake | Ratio |
|---|---|---|---|
| Total Cost of Ownership (5 years, 100TB)(USD) | $1,200,000-$1,800,000 | — | — |
| Required IT Staff (FTE)(people) | 5-10 FTE | — | — |
| Data Access Latency(milliseconds) | 20-50 ms | — | — |
| Scalability Limit(petabytes) | Limited by cluster (typically 10-100 PB) | — | — |
| Scale-Up Time(hours) | 24-72 hours | — | — |
| Availability SLA(percent uptime) | 95-99% (cluster-dependent) | — | — |
| Storage Cost (monthly, 100TB)(USD) | $12,500-$25,000 | — | — |
| Processing Latency(milliseconds) | 180-3600 seconds | — | — |
| Throughput (Records/Second)(million records/sec) | 100K-500K | — | — |
| Memory Usage per Node(GB) | 8-32 GB | — | — |
| Minimum Cluster Size(nodes) | 3-5 nodes | 1 (virtual warehouse) | |
| Supported Languages(count) | 2 (Java, Scala) | — | — |
| GitHub Stars (2025)(stars) | 12.4K | — | — |
| Optimal Dataset Size(GB minimum) | 100+ GB batches | — | — |
| Processing Speed (Same 1TB dataset)(seconds) | 300-600 seconds (disk-based) | — | — |
| Initial Setup Time to Production(weeks) | 8-12 weeks | — | — |
| Processing Speed vs MapReduce Baseline(times faster) | 1x (baseline) | — | — |
| Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) | $2,500-5,000 (infrastructure only) | — | — |
| Required Team Skills (FTE equivalents for operations)(FTE) | 2-3 dedicated engineers | — | — |
| SQL Query Standards Compliance(% ANSI SQL support) | Hive SQL (65% ANSI) | — | — |
| Query Latency (median, standard ETL workload)(seconds) | 45-120 seconds | — | — |
| Built-in Collaboration Tools (notebooks, dashboards, repos)(count) | 0 (requires third-party) | — | — |
| Time to Query 1TB Dataset(seconds) | 10-30 seconds (with Spark) | — | — |
| Infrastructure Cost (Annual, 50TB dataset)(USD) | $150,000-$250,000 | — | — |
| Infrastructure Cost (Annual, 500TB dataset)(USD) | $200,000-$400,000 | — | — |
| Setup Time to First Query(minutes) | 30-90 days (cluster + network + security) | — | — |
| Maximum Unstructured Data Support(% of typical use cases) | 90% (native HDFS support for any file type) | — | — |
| Admin/DevOps Time Required (Monthly)(hours) | 40-80 hours (patching, monitoring, scaling) | — | — |
| Maximum Query Parallelism(number of nodes) | 10,000+ (custom hardware limits) | — | — |
| Processing Speed (Average Query)(seconds) | 300-600 seconds | — | — |
| Memory Requirement (Per Node)(GB) | 4-8 GB | — | — |
| Supported Programming Languages(languages) | Java, Scala | — | — |
| Market Adoption by Fortune 500(percent) | 35% | — | — |
| Typical Cluster Cost (100-node setup)(USD annual) | $180,000-250,000 | — | — |
| Initial Setup Time(hours) | 3 weeks | 0.1 weeks (24 hours) | |
| Query Latency (1TB scan)(seconds) | 120-300 seconds | — | — |
| Total Cost of Ownership (100TB/year)(USD) | $150,000-$400,000 | — | — |
| Team Expertise Required(months to proficiency) | 6-12 months | — | — |
| Supported Processing Models(count) | 4+ (batch, streaming, graph, ML) | — | — |
| Initial Deployment Time(minutes) | 4-8 weeks | — | — |
| Processing Speed (Iterative ML)(x relative to baseline) | 1x (MapReduce baseline) | — | — |
| SQL Query Latency (100GB dataset)(seconds) | 15-45 seconds (Hive) | — | — |
| Annual Cost (100TB/year, 5-node baseline)(USD thousands) | $180,000-$250,000 | — | — |
| Query Performance (1TB dataset)(seconds) | 120-300 seconds | — | — |
| Annual TCO (100TB workload)(USD) | $150,000-$300,000 | — | — |
| Minimum Team Size(people) | 4-8 (DevOps, engineers, admins) | — | — |
| Maximum Query Concurrency(concurrent queries) | 50-100 per cluster | — | — |
| Storage Cost (per TB/month)(USD) | $12-20 | $23 (on-demand) | |
| Data Locality Advantage(% bandwidth savings) | 40-60% reduction in network I/O | — | — |
| Custom Algorithm Support (1-5 scale)(capability score) | 5 (full MapReduce/Spark) | — | — |
| Query Performance (10TB TPC-DS benchmark)(seconds) | 95 seconds | 5 seconds | |
| Annual Cost (100TB, 24/7 usage)(USD) | $120,000 | $200,000 | |
| Data Recovery (Time Travel)(days) | Manual snapshots only | 90 days automatic | — |
| Required DevOps Team Size(FTE) | 3-5 engineers | 0.5 engineers | |
| Community Size (GitHub stars)(stars) | 14,200 stars | 2,800 stars | |
| Starting Monthly Cost(USD) | $2,000-$5,000 | $2,000-$5,000 | |
| Setup Time(minutes) | 1-3 days | 1-3 days | |
| Query Performance (TPC-DS)(seconds) | 15-20 | 15-20 | |
| ML/AI Integration Score(out of 10) | 4/10 | 4/10 | |
| Global Enterprise Customers(count (2026)) | 10,000+ | 10,000+ | |
| Supported Cloud Providers(number of platforms) | 3 (AWS, Azure, GCP) | 3 (AWS, Azure, GCP) | |
| Setup Time to First Query(minutes) | 20-30 minutes | 20-30 minutes | |
| Data Marketplace Size(number of datasets) | 1,000+ datasets | 1,000+ datasets | |
| Annual Customer Growth Rate (2025)(percent) | 22% YoY | 22% YoY | |
| Average Enterprise Contract Value(USD thousands per year) | $200-500 | $200-500 | |
| Base Cost per TB (Monthly)(USD) | $4-6 | $4-6 | |
| Available Cloud Providers(count) | AWS, Azure, GCP | AWS, Azure, GCP | |
| Average Query Response Time(seconds) | 2-4 seconds | 2-4 seconds | |
| Time to Production (median)(weeks) | 1-3 weeks | 1-3 weeks | |
| Market Share 2026(percent) | 32% | 32% | |
| Query Latency (1 billion rows)(seconds) | 30 seconds | 30 seconds | |
| Monthly Cost (100 GB compressed)(USD) | $1,500 | $1,500 | |
| Ingestion Throughput(events/sec) | 100,000 events/sec | 100,000 events/sec | |
| Data Retention for Time-Travel(days) | 90 days | 90 days | |
| Compression Ratio(ratio) | 4:1 to 8:1 | 4:1 to 8:1 | |
| Learning Curve (1-10 scale)(difficulty level) | 3/10 (very easy) | 3/10 (very easy) | |
| Data Warehouse Query Speed (Typical)(seconds) | <5 seconds | <5 seconds | |
| Query Latency (1TB dataset)(seconds) | 30-120 seconds | 30-120 seconds | |
| Deployment Time(months) | 0.3-0.5 weeks (1-2 days) | 0.3-0.5 weeks (1-2 days) | |
| Annual Cost (100TB storage, 10 users)(USD) | $120,000-180,000 | $120,000-180,000 | |
| Maximum Scalability(concurrent container instances) | Up to 50+ PB (cloud limits) | Up to 50+ PB (cloud limits) | |
| Time to First Query (production)(days) | 1-3 days | 1-3 days | |
| Required Technical Expertise Level(years experience needed) | 1-2 years (SQL knowledge) | 1-2 years (SQL knowledge) | |
| Annual License Cost (100TB data)(USD) | $240,000 | $240,000 | |
| Uptime SLA Guarantee(percent) | 99.99% | 99.99% | |
| Query Response Time (10TB scan)(seconds) | 8.2 | 8.2 | |
| Data Format Support Count(formats) | 8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images) | 8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images) | |
| Available Integrations(count) | 600+ | 600+ | |
| Time to Production(days) | 0.5 | 0.5 | |
| Query Latency (Typical)(milliseconds) | 1,000-10,000ms | 1,000-10,000ms | |
| SQL Standard Compliance(percent) | 95% (full ANSI) | 95% (full ANSI) | |
| Enterprise Customers (2025)(count) | ~10,000 enterprises | ~10,000 enterprises | |
| Base Setup Cost (Annual)(USD) | $10,000-1,000,000 (credits-based) | $10,000-1,000,000 (credits-based) | |
| Time to Insight (Complex Query)(seconds) | 3-15 (depends on data size) | 3-15 (depends on data size) | |
| Maximum Daily Data Volume(terabytes) | Unlimited (petabyte-scale) | Unlimited (petabyte-scale) | |
| Operational Complexity (1-10 scale)(complexity score) | 3/10 (managed cloud service) | 3/10 (managed cloud service) | |
| SQL Query Performance (1TB dataset)(seconds) | 2-5 seconds | 2-5 seconds | |
| Base Monthly Cost (minimum)(USD) | $120-240 | $120-240 | |
| Data Format Support(format types) | Structured (optimized for tables/CSV/JSON) | Structured (optimized for tables/CSV/JSON) | |
| Concurrent Users Support(users) | Unlimited (multi-cluster shared warehouse) | Unlimited (multi-cluster shared warehouse) | |
| Data Warehouse Setup Time(minutes) | 5-10 minutes | 5-10 minutes | |
| Global Market Share (2024)(%) | 32% of cloud data warehouse market | 32% of cloud data warehouse market | |
| ML Model Training Cost Efficiency(relative cost index) | 2.8x baseline (external ML tools required) | 2.8x baseline (external ML tools required) | |
| TPC-DS 100TB Query Performance(seconds) | 38 seconds | 38 seconds | |
| Base Hourly Cost (2-node cluster)(USD/hour) | $4.00-$6.00 (Medium warehouse) | $4.00-$6.00 (Medium warehouse) | |
| Query Performance (TPC-DS 100GB)(seconds) | ~14 seconds | ~14 seconds | |
| Scaling Adjustment Time(minutes) | ~1 (auto-scaling, no downtime) | ~1 (auto-scaling, no downtime) | |
| Maximum Single Query Data Scanned(petabytes) | 20+ | 20+ | |
| Cloud Providers Supported(count) | 3 (AWS, Azure, GCP) | 3 (AWS, Azure, GCP) | |
| Annual Contract Discount(percent) | Up to 20% | Up to 20% | |
| Configuration Tuning Required(hours (estimated)) | 4-8 (clustering hints optional) | 4-8 (clustering hints optional) | |
| Starting Monthly Cost (10GB active data)(USD) | $480 | $480 | |
| SQL Query Performance (TPC-DS Benchmark)(seconds) | 28 | 28 | |
| BI Tool Native Connectors(count) | 150+ | 150+ | |
| Maximum Concurrent Queries Per Warehouse(queries) | 8-128 (warehouse-dependent) | 8-128 (warehouse-dependent) | |
| Customer Satisfaction Rating (G2 2025)(percent) | 85% | 85% | |
| Setup Complexity (1-10 scale)(complexity score) | 4 | 4 | |
| Annual TCO (100TB storage, average usage)(USD) | $260,000 | $260,000 | |
| TPC-DS Query Benchmark (100GB dataset)(seconds) | 38 | 38 | |
| Setup Time to Production(minutes) | 10-15 hours | 10-15 hours | |
| Data Marketplace Size(datasets) | 1,500+ | 1,500+ | |
| Reserved Instance Discount(percent) | None (on-demand only) | None (on-demand only) | |
| Query Latency (P99 percentile)(milliseconds) | 2500ms | 2500ms | |
| Maximum Ingestion Rate(events/second) | 500,000 | 500,000 | |
| Storage Cost(USD per TB per month) | $50 | $50 | |
| Concurrent Query Capacity(concurrent users) | 1000+ | 1000+ | |
| Time to First Query(minutes) | 5 (account creation) | 5 (account creation) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
AH
1Apache HadoopSnowflake leads
Apache Hadoop
Attribute
Snowflake
- Distributed computing framework (MapReduce-based)ArchitectureCloud-native SQL data warehouse (MPP)(winner)
- 45-120 secondsQuery Speed (10TB dataset)2-8 seconds(winner)
- $80,000-150,000(winner)Infrastructure Cost (annual, 100TB)$150,000-250,000
- High (requires DevOps expertise)Setup & Management ComplexityLow (managed service)(winner)
- Tightly coupledCompute & Storage SeparationIndependent scaling(winner)
- Limited (Hive SQL, slower)SQL SupportNative ANSI SQL support(winner)
- 2-4 weeksTime to First Query (deployment)24-48 hours(winner)
- Architecture
Apache Hadoop
Distributed computing framework (MapReduce-based)
Snowflake
Cloud-native SQL data warehouse (MPP)(winner)
- Query Speed (10TB dataset)
Apache Hadoop
45-120 seconds
Snowflake
2-8 seconds(winner)
- Infrastructure Cost (annual, 100TB)
Apache Hadoop
$80,000-150,000(winner)
Snowflake
$150,000-250,000
- Setup & Management Complexity
Apache Hadoop
High (requires DevOps expertise)
Snowflake
Low (managed service)(winner)
- Compute & Storage Separation
Apache Hadoop
Tightly coupled
Snowflake
Independent scaling(winner)
- SQL Support
Apache Hadoop
Limited (Hive SQL, slower)
Snowflake
Native ANSI SQL support(winner)
- Time to First Query (deployment)
Apache Hadoop
2-4 weeks
Snowflake
24-48 hours(winner)
Full Comparison
| Attribute | Apache Hadoop | Snowflake |
|---|---|---|
| Total Cost of Ownership (5 years, 100TB)(USD) | $1,200,000-$1,800,000 | — |
| Storage Cost (monthly, 100TB)(USD) | $12,500-$25,000 | — |
| Infrastructure Cost (Annual, 50TB dataset)(USD) | $150,000-$250,000 | — |
| Infrastructure Cost (Annual, 500TB dataset)(USD) | $200,000-$400,000 | — |
| Typical Cluster Cost (100-node setup)(USD annual) | $180,000-250,000 | — |
Show 6 more attributesTotal Cost of Ownership (100TB/year)(USD) $150,000-$400,000 — Annual Cost (100TB/year, 5-node baseline)(USD thousands) $180,000-$250,000 — Annual TCO (100TB workload)(USD) $150,000-$300,000 — Storage Cost (per TB/month)(USD) $12-20 $23 (on-demand) Annual Cost (100TB storage, 10 users)(USD) $120,000-180,000 — Annual TCO (100TB storage, average usage)(USD) $260,000 — | ||
| Setup Time(hours) | 28-84 days | 15 minutes(winner) |
| Initial Deployment Time(minutes) | 4-8 weeks | — |
| Time to Production (median)(weeks) | 1-3 weeks | — |
| Time to First Query (production)(days) | 1-3 days | — |
| Required IT Staff (FTE)(people) | 5-10 FTE | — |
| Required Team Skills (FTE equivalents for operations)(FTE) | 2-3 dedicated engineers | — |
| Admin/DevOps Time Required (Monthly)(hours) | 40-80 hours (patching, monitoring, scaling) | — |
| Team Expertise Required(months to proficiency) | 6-12 months | — |
| Cluster Auto-scaling Capability(supported) | Manual (requires YARN configuration) | — |
Show 4 more attributesMinimum Team Size(people) 4-8 (DevOps, engineers, admins) — Required DevOps Team Size(FTE) 3-5 engineers 0.5 engineers Scaling Adjustment Time(minutes) ~1 (auto-scaling, no downtime) — Configuration Tuning Required(hours (estimated)) 4-8 (clustering hints optional) — | ||
| Data Access Latency(milliseconds) | 20-50 ms | — |
| Processing Latency(milliseconds) | 180-3600 seconds | — |
| Throughput (Records/Second)(million records/sec) | 100K-500K | — |
| Processing Speed (Same 1TB dataset)(seconds) | 300-600 seconds (disk-based) | — |
| Processing Speed vs MapReduce Baseline(times faster) | 1x (baseline) | — |
Show 29 more attributesQuery Latency (median, standard ETL workload)(seconds) 45-120 seconds — Time to Query 1TB Dataset(seconds) 10-30 seconds (with Spark) — Processing Speed (Average Query)(seconds) 300-600 seconds — Query Latency (1TB scan)(seconds) 120-300 seconds — Processing Speed (Iterative ML)(x relative to baseline) 1x (MapReduce baseline) — SQL Query Latency (100GB dataset)(seconds) 15-45 seconds (Hive) — Query Performance (1TB dataset)(seconds) 120-300 seconds — Data Locality Advantage(% bandwidth savings) 40-60% reduction in network I/O — Query Performance (10TB TPC-DS benchmark)(seconds) 95 seconds 5 seconds Query Performance (TPC-DS)(seconds) 15-20 — 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 — Ingestion Throughput(events/sec) 100,000 events/sec — Data Warehouse Query Speed (Typical)(seconds) <5 seconds — Query Latency (1TB dataset)(seconds) 30-120 seconds — 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 — TPC-DS 100TB Query Performance(seconds) 38 seconds — Query Performance (TPC-DS 100GB)(seconds) ~14 seconds — SQL Query Performance (TPC-DS Benchmark)(seconds) 28 — Maximum Concurrent Queries Per Warehouse(queries) 8-128 (warehouse-dependent) — TPC-DS Query Benchmark (100GB dataset)(seconds) 38 — Query Latency (P99 percentile)(milliseconds) 2500ms — | ||
| Scalability Limit(petabytes) | Limited by cluster (typically 10-100 PB) | — |
| Scale-Up Time(hours) | 24-72 hours | — |
| Maximum Query Parallelism(number of nodes) | 10,000+ (custom hardware limits) | — |
| Data Storage Capacity(PB) | Unlimited (cluster-dependent) | — |
| Maximum Query Concurrency(concurrent queries) | 50-100 per cluster | — |
| Maximum Concurrent Users(users) | Unlimited | — |
Show 3 more attributesMaximum Daily Data Volume(terabytes) Unlimited (petabyte-scale) — Concurrent Users Support(users) Unlimited (multi-cluster shared warehouse) — Concurrent Query Capacity(concurrent users) 1000+ — | ||
| Availability SLA(percent uptime) | 95-99% (cluster-dependent) | — |
| Fault Tolerance Mechanism | Task re-execution + HDFS replication | — |
| Fault Tolerance Method(mechanism) | Replication (3x copies) | — |
| Uptime SLA Guarantee(percent) | 99.99% | — |
| Memory Usage per Node(GB) | 8-32 GB | — |
| Minimum Cluster Size(nodes) | 3-5 nodes | 1 (virtual warehouse)(winner) |
| Memory Requirement (Per Node)(GB) | 4-8 GB | — |
| Supported Cloud Providers(number of platforms) | 3 (AWS, Azure, GCP) | — |
| Available Cloud Providers(count) | AWS, Azure, GCP | — |
| Cloud Platform Support | AWS, Azure, GCP | — |
| Supported Languages(count) | 2 (Java, Scala) | — |
| GitHub Stars (2025)(stars) | 12.4K | — |
| Optimal Dataset Size(GB minimum) | 100+ GB batches | — |
| Initial Setup Time to Production(weeks) | 8-12 weeks | — |
| On-Premises Deployment Option | Yes (full control) | — |
| Setup Time to First Query(minutes) | 20-30 minutes | — |
| Time to Production(days) | 0.5 | — |
| Cloud Providers Supported(count) | 3 (AWS, Azure, GCP) | — |
Show 1 more attributeSetup Time to Production(minutes) 10-15 hours — | ||
| Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) | $2,500-5,000 (infrastructure only) | — |
| Starting Monthly Cost(USD) | $2,000-$5,000 | — |
| 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 | — |
Show 9 more attributesMonthly Cost (100 GB compressed)(USD) $1,500 — 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 — 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 — Reserved Instance Discount(percent) None (on-demand only) — Storage Cost(USD per TB per month) $50 — | ||
| SQL Query Standards Compliance(% ANSI SQL support) | Hive SQL (65% ANSI) | — |
| Built-in Collaboration Tools (notebooks, dashboards, repos)(count) | 0 (requires third-party) | — |
| Real-time Streaming Capability(latency (ms)) | Not supported | — |
| SQL Query Support | Hive SQL (SQL92, limited optimizations) | ANSI SQL with advanced optimizations |
| Data Recovery (Time Travel)(days) | Manual snapshots only | 90 days automatic |
Show 7 more attributesData Sharing Standard(technology) Snowflake Marketplace (proprietary) — 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) — SQL Standard Compliance(percent) 95% (full ANSI) — Native ML/AI Capabilities Limited (external integration required) — | ||
| Setup Time to First Query(minutes) | 30-90 days (cluster + network + security) | — |
| Initial Setup Time(hours) | 3 weeks | 0.1 weeks (24 hours)(winner) |
| Data Warehouse Setup Time(minutes) | 5-10 minutes | — |
| Maximum Unstructured Data Support(% of typical use cases) | 90% (native HDFS support for any file type) | — |
| Real-time Analytics Capability | Yes (sub-second latency) | — |
| Data Residency Control(null) | Complete (on-premises or self-managed cloud) | — |
| First Release(year) | 2011 | — |
| Supported Programming Languages(languages) | Java, Scala | — |
| SQL Compliance | ANSI SQL compliant | — |
| Market Adoption by Fortune 500(percent) | 35% | — |
| Supported Processing Models(count) | 4+ (batch, streaming, graph, ML) | — |
| Vendor Lock-in Risk(risk level) | Low (portable open-source) | — |
| Supported Data Formats(types) | Structured (Parquet, CSV, JSON) | — |
| Deployment Options(count) | Cloud-only (SaaS) | — |
| Data Format Support(format types) | Structured (optimized for tables/CSV/JSON) | — |
| Native Row/Column-Level Access Control(supported) | No (requires Ranger) | — |
| Collaborative Notebooks with Version Control(native support) | No (requires Jupyter/Git separately) | — |
| Custom Algorithm Support (1-5 scale)(capability score) | 5 (full MapReduce/Spark) | — |
| Annual Cost (100TB, 24/7 usage)(USD) | $120,000(winner) | $200,000 |
| Licensing Model | Consumption-based (compute + storage) | — |
| Compute & Storage Coupling | Tightly coupled (scale together) | Fully independent (separate pricing) |
| Compute-Storage Decoupling | Complete separation | — |
| Compute-Storage Decoupling | Independent scaling | — |
| Community Size (GitHub stars)(stars) | 14,200 stars(winner) | 2,800 stars |
| Enterprise Customers (2025)(count) | ~10,000 enterprises | — |
| Setup Time(minutes) | 1-3 days | — |
| Customer Satisfaction Rating (G2 2025)(percent) | 85% | — |
| ML/AI Integration Score(out of 10) | 4/10 | — |
| Native ML Framework Integration | Cortex AI (basic) | — |
| Global Enterprise Customers(count (2026)) | 10,000+ | — |
| Market Share 2026(percent) | 32% | — |
| Global Market Share (2024)(%) | 32% of cloud data warehouse market | — |
| Multi-Language Support(languages) | SQL primarily | — |
| Data Marketplace Size(number of datasets) | 1,000+ datasets | — |
| Annual Customer Growth Rate (2025)(percent) | 22% YoY | — |
| Compression Ratio(ratio) | 4:1 to 8:1 | — |
| Learning Curve (1-10 scale)(difficulty level) | 3/10 (very easy) | — |
| Required Technical Expertise Level(years experience needed) | 1-2 years (SQL knowledge) | — |
| Deployment Time(months) | 0.3-0.5 weeks (1-2 days) | — |
| Supported Query Languages(count) | SQL, Python, Java, JavaScript, Scala | — |
| Available Integrations(count) | 600+ | — |
| Data Marketplace Size(datasets) | 1,500+ | — |
| Operational Complexity (1-10 scale)(complexity score) | 3/10 (managed cloud service) | — |
| ML Model Training Cost Efficiency(relative cost index) | 2.8x baseline (external ML tools required) | — |
| Maximum Single Query Data Scanned(petabytes) | 20+ | — |
| Data Format Lock-in Risk | High (proprietary format) | — |
| BI Tool Native Connectors(count) | 150+ | — |
| Setup Complexity (1-10 scale)(complexity score) | 4 | — |
| Maximum Ingestion Rate(events/second) | 500,000 | — |
| Time to First Query(minutes) | 5 (account creation) | — |
AApache Hadoop
SnowflakeTotal Cost of Ownership (5 years, 100TB)(USD)
$1,200,000-$1,800,000
—
Storage Cost (monthly, 100TB)(USD)
$12,500-$25,000
—
Infrastructure Cost (Annual, 50TB dataset)(USD)
$150,000-$250,000
—
Infrastructure Cost (Annual, 500TB dataset)(USD)
$200,000-$400,000
—
Typical Cluster Cost (100-node setup)(USD annual)
$180,000-250,000
—
Show 6 more attributes
Total Cost of Ownership (100TB/year)(USD)
$150,000-$400,000
—
Annual Cost (100TB/year, 5-node baseline)(USD thousands)
$180,000-$250,000
—
Annual TCO (100TB workload)(USD)
$150,000-$300,000
—
Storage Cost (per TB/month)(USD)
$12-20
$23 (on-demand)
Annual Cost (100TB storage, 10 users)(USD)
$120,000-180,000
—
Annual TCO (100TB storage, average usage)(USD)
$260,000
—
Setup Time(hours)
28-84 days
15 minutes
Initial Deployment Time(minutes)
4-8 weeks
—
Time to Production (median)(weeks)
1-3 weeks
—
Time to First Query (production)(days)
1-3 days
—
Required IT Staff (FTE)(people)
5-10 FTE
—
Required Team Skills (FTE equivalents for operations)(FTE)
2-3 dedicated engineers
—
Admin/DevOps Time Required (Monthly)(hours)
40-80 hours (patching, monitoring, scaling)
—
Team Expertise Required(months to proficiency)
6-12 months
—
Cluster Auto-scaling Capability(supported)
Manual (requires YARN configuration)
—
Show 4 more attributes
Minimum Team Size(people)
4-8 (DevOps, engineers, admins)
—
Required DevOps Team Size(FTE)
3-5 engineers
0.5 engineers
Scaling Adjustment Time(minutes)
~1 (auto-scaling, no downtime)
—
Configuration Tuning Required(hours (estimated))
4-8 (clustering hints optional)
—
Data Access Latency(milliseconds)
20-50 ms
—
Processing Latency(milliseconds)
180-3600 seconds
—
Throughput (Records/Second)(million records/sec)
100K-500K
—
Processing Speed (Same 1TB dataset)(seconds)
300-600 seconds (disk-based)
—
Processing Speed vs MapReduce Baseline(times faster)
1x (baseline)
—
Show 29 more attributes
Query Latency (median, standard ETL workload)(seconds)
45-120 seconds
—
Time to Query 1TB Dataset(seconds)
10-30 seconds (with Spark)
—
Processing Speed (Average Query)(seconds)
300-600 seconds
—
Query Latency (1TB scan)(seconds)
120-300 seconds
—
Processing Speed (Iterative ML)(x relative to baseline)
1x (MapReduce baseline)
—
SQL Query Latency (100GB dataset)(seconds)
15-45 seconds (Hive)
—
Query Performance (1TB dataset)(seconds)
120-300 seconds
—
Data Locality Advantage(% bandwidth savings)
40-60% reduction in network I/O
—
Query Performance (10TB TPC-DS benchmark)(seconds)
95 seconds
5 seconds
Query Performance (TPC-DS)(seconds)
15-20
—
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
—
Ingestion Throughput(events/sec)
100,000 events/sec
—
Data Warehouse Query Speed (Typical)(seconds)
<5 seconds
—
Query Latency (1TB dataset)(seconds)
30-120 seconds
—
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
—
TPC-DS 100TB Query Performance(seconds)
38 seconds
—
Query Performance (TPC-DS 100GB)(seconds)
~14 seconds
—
SQL Query Performance (TPC-DS Benchmark)(seconds)
28
—
Maximum Concurrent Queries Per Warehouse(queries)
8-128 (warehouse-dependent)
—
TPC-DS Query Benchmark (100GB dataset)(seconds)
38
—
Query Latency (P99 percentile)(milliseconds)
2500ms
—
Scalability Limit(petabytes)
Limited by cluster (typically 10-100 PB)
—
Scale-Up Time(hours)
24-72 hours
—
Maximum Query Parallelism(number of nodes)
10,000+ (custom hardware limits)
—
Data Storage Capacity(PB)
Unlimited (cluster-dependent)
—
Maximum Query Concurrency(concurrent queries)
50-100 per cluster
—
Maximum Concurrent Users(users)
Unlimited
—
Show 3 more attributes
Maximum Daily Data Volume(terabytes)
Unlimited (petabyte-scale)
—
Concurrent Users Support(users)
Unlimited (multi-cluster shared warehouse)
—
Concurrent Query Capacity(concurrent users)
1000+
—
Availability SLA(percent uptime)
95-99% (cluster-dependent)
—
Fault Tolerance Mechanism
Task re-execution + HDFS replication
—
Fault Tolerance Method(mechanism)
Replication (3x copies)
—
Uptime SLA Guarantee(percent)
99.99%
—
Memory Usage per Node(GB)
8-32 GB
—
Minimum Cluster Size(nodes)
3-5 nodes
1 (virtual warehouse)
Memory Requirement (Per Node)(GB)
4-8 GB
—
Supported Cloud Providers(number of platforms)
3 (AWS, Azure, GCP)
—
Available Cloud Providers(count)
AWS, Azure, GCP
—
Cloud Platform Support
AWS, Azure, GCP
—
Supported Languages(count)
2 (Java, Scala)
—
GitHub Stars (2025)(stars)
12.4K
—
Optimal Dataset Size(GB minimum)
100+ GB batches
—
Initial Setup Time to Production(weeks)
8-12 weeks
—
On-Premises Deployment Option
Yes (full control)
—
Setup Time to First Query(minutes)
20-30 minutes
—
Time to Production(days)
0.5
—
Cloud Providers Supported(count)
3 (AWS, Azure, GCP)
—
Show 1 more attribute
Setup Time to Production(minutes)
10-15 hours
—
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD)
$2,500-5,000 (infrastructure only)
—
Starting Monthly Cost(USD)
$2,000-$5,000
—
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
—
Show 9 more attributes
Monthly Cost (100 GB compressed)(USD)
$1,500
—
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
—
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
—
Reserved Instance Discount(percent)
None (on-demand only)
—
Storage Cost(USD per TB per month)
$50
—
SQL Query Standards Compliance(% ANSI SQL support)
Hive SQL (65% ANSI)
—
Built-in Collaboration Tools (notebooks, dashboards, repos)(count)
0 (requires third-party)
—
Real-time Streaming Capability(latency (ms))
Not supported
—
SQL Query Support
Hive SQL (SQL92, limited optimizations)
ANSI SQL with advanced optimizations
Data Recovery (Time Travel)(days)
Manual snapshots only
90 days automatic
Show 7 more attributes
Data Sharing Standard(technology)
Snowflake Marketplace (proprietary)
—
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)
—
SQL Standard Compliance(percent)
95% (full ANSI)
—
Native ML/AI Capabilities
Limited (external integration required)
—
Setup Time to First Query(minutes)
30-90 days (cluster + network + security)
—
Initial Setup Time(hours)
3 weeks
0.1 weeks (24 hours)
Data Warehouse Setup Time(minutes)
5-10 minutes
—
Maximum Unstructured Data Support(% of typical use cases)
90% (native HDFS support for any file type)
—
Real-time Analytics Capability
Yes (sub-second latency)
—
Data Residency Control(null)
Complete (on-premises or self-managed cloud)
—
First Release(year)
2011
—
Supported Programming Languages(languages)
Java, Scala
—
SQL Compliance
ANSI SQL compliant
—
Market Adoption by Fortune 500(percent)
35%
—
Supported Processing Models(count)
4+ (batch, streaming, graph, ML)
—
Vendor Lock-in Risk(risk level)
Low (portable open-source)
—
Supported Data Formats(types)
Structured (Parquet, CSV, JSON)
—
Deployment Options(count)
Cloud-only (SaaS)
—
Data Format Support(format types)
Structured (optimized for tables/CSV/JSON)
—
Native Row/Column-Level Access Control(supported)
No (requires Ranger)
—
Collaborative Notebooks with Version Control(native support)
No (requires Jupyter/Git separately)
—
Custom Algorithm Support (1-5 scale)(capability score)
5 (full MapReduce/Spark)
—
Annual Cost (100TB, 24/7 usage)(USD)
$120,000
$200,000
Licensing Model
Consumption-based (compute + storage)
—
Compute & Storage Coupling
Tightly coupled (scale together)
Fully independent (separate pricing)
Compute-Storage Decoupling
Complete separation
—
Compute-Storage Decoupling
Independent scaling
—
Community Size (GitHub stars)(stars)
14,200 stars
2,800 stars
Enterprise Customers (2025)(count)
~10,000 enterprises
—
Setup Time(minutes)
1-3 days
—
Customer Satisfaction Rating (G2 2025)(percent)
85%
—
ML/AI Integration Score(out of 10)
4/10
—
Native ML Framework Integration
Cortex AI (basic)
—
Global Enterprise Customers(count (2026))
10,000+
—
Market Share 2026(percent)
32%
—
Global Market Share (2024)(%)
32% of cloud data warehouse market
—
Multi-Language Support(languages)
SQL primarily
—
Data Marketplace Size(number of datasets)
1,000+ datasets
—
Annual Customer Growth Rate (2025)(percent)
22% YoY
—
Compression Ratio(ratio)
4:1 to 8:1
—
Learning Curve (1-10 scale)(difficulty level)
3/10 (very easy)
—
Required Technical Expertise Level(years experience needed)
1-2 years (SQL knowledge)
—
Deployment Time(months)
0.3-0.5 weeks (1-2 days)
—
Supported Query Languages(count)
SQL, Python, Java, JavaScript, Scala
—
Available Integrations(count)
600+
—
Data Marketplace Size(datasets)
1,500+
—
Operational Complexity (1-10 scale)(complexity score)
3/10 (managed cloud service)
—
ML Model Training Cost Efficiency(relative cost index)
2.8x baseline (external ML tools required)
—
Maximum Single Query Data Scanned(petabytes)
20+
—
Data Format Lock-in Risk
High (proprietary format)
—
BI Tool Native Connectors(count)
150+
—
Setup Complexity (1-10 scale)(complexity score)
4
—
Maximum Ingestion Rate(events/second)
500,000
—
Time to First Query(minutes)
5 (account creation)
—
Pros & Cons
10 pros·6 cons across both
AH
AH
Apache Hadoop
+5-3
Pros
- Open-source with zero licensing costs
- Handles unstructured and semi-structured data efficiently
- Fault-tolerant architecture with automatic replication
- Highly customizable for specific workloads
- No vendor lock-in; runs on commodity hardware
Cons
- Steep learning curve requiring specialized DevOps/Java expertise
- Slower query performance (batch processing model, not optimized for interactive queries)
- Complex cluster management and maintenance overhead
Snowflake
+5-3
Pros
- Industry-leading query performance (10-50x faster than Hadoop for SQL)
- Zero-copy data cloning and time-travel for data recovery
- Automatic scaling and multi-cluster capabilities without downtime
- Native support for semi-structured data (JSON, Parquet) without ETL
- Minimal DevOps overhead with fully managed infrastructure
Cons
- Significant cloud costs ($2-4 per credit hour; $150K-300K annually for medium organizations)
- Data egress charges when exporting data outside cloud provider
- Vendor lock-in; migration away requires significant effort
Frequently Asked Questions
5 questions
Choose Hadoop if you process petabyte-scale unstructured data, require on-premises deployment for compliance, have existing Hadoop ecosystem investments, or need absolute cost minimization for large sustained workloads. Hadoop excels at batch processing (ETL pipelines, log analysis, machine learning training) where latency isn't critical.
Resources & Learn More
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