software
Hadoop vs Databricks 2026: Speed, Cost & Setup
Hadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in collaboration, MLOps, and governance tools. Databricks eliminates much of Hadoop's operational complexity but at a higher cost.
AH
Apache Hadoop
Open-source distributed computing framework using MapReduce and HDFS for batch processing
Organizations with existing Hadoop investments, on-premises requirements, and dedicated data infrastructure teams managing 500+ nodes
Score63%
VS
Databricks
Managed cloud analytics platform built on Apache Spark with native collaboration, MLOps, and governance
Mid-market to enterprise teams needing rapid deployment, collaborative analytics, built-in ML capabilities, and integrated governance—particularly those already using cloud infrastructure
Score67%
Quick Answer
AI SummaryHadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in collaboration, MLOps, and governance tools. Databricks eliminates much of Hadoop's operational complexity but at a higher cost.
Our Verdict
AI-assistedChoose Hadoop if you have legacy systems deeply integrated with HDFS, need maximum cost control for commodity hardware, or operate entirely on-premises with strict data residency requirements. Choose Databricks if you prioritize time-to-value, need collaborative analytics with built-in ML/BI tools, or want modern governance—it pays for itself through reduced ops overhead and 3-5x faster time-to-insight for most enterprises.
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A
Apache Hadoop
5.4/10
Databricks
9.6/10
A
Choose Apache Hadoop if
Organizations with existing Hadoop investments, on-premises requirements, and dedicated data infrastructure teams managing 500+ nodes
Choose Databricks if
Best pickMid-market to enterprise teams needing rapid deployment, collaborative analytics, built-in ML capabilities, and integrated governance—particularly those already using cloud infrastructure
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Key Differences at a Glance
- Deployment Model:✓ Databricks wins(Fully managed SaaS (AWS, Azure, GCP) vs Self-managed on-premises or IaaS)
- Processing Engine:✓ Databricks wins(Apache Spark (in-memory, 100x faster for iterative workloads) vs MapReduce (batch-oriented, 2-10x slower than Spark))
- Setup Time:✓ Databricks wins(15 minutes to launch workspace vs 4-8 weeks for production cluster)
Key Facts & Figures
95 numeric metrics compared
| Metric | Apache Hadoop | Databricks | 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 | — | — |
| 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 | 1-2 weeks | |
| Processing Speed vs MapReduce Baseline(times faster) | 1x (baseline) | 10-100x faster | |
| Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) | $2,500-5,000 (infrastructure only) | $550-850 | |
| Required Team Skills (FTE equivalents for operations)(FTE) | 2-3 dedicated engineers | 0.25 (minimal management) | |
| SQL Query Standards Compliance(% ANSI SQL support) | Hive SQL (65% ANSI) | Full ANSI SQL (100%) | |
| Query Latency (median, standard ETL workload)(seconds) | 45-120 seconds | 3.5-8 seconds | |
| Built-in Collaboration Tools (notebooks, dashboards, repos)(count) | 0 (requires third-party) | Notebooks, Dashboards, SQL Editor, Repos, MLflow | |
| Community Size (GitHub Stars)(stars) | 13,500 stars (hadoop/hadoop) | 8,200 stars (databricks/databricks-cli) | |
| 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(count) | Java, Scala | — | — |
| Market Adoption by Fortune 500(percent) | 35% | — | — |
| Typical Cluster Cost (100-node setup)(USD annual) | $180,000-250,000 | — | — |
| Initial Setup Time(minutes) | 4-12 weeks | — | — |
| 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(weeks) | 4-8 weeks | 0.25 weeks (15 minutes) | |
| Processing Speed (Iterative ML)(x relative to baseline) | 1x (MapReduce baseline) | 50-100x faster (Spark + Photon) | |
| SQL Query Latency (100GB dataset)(seconds) | 15-45 seconds (Hive) | 0.5-3 seconds (Photon) | |
| Annual Cost (100TB/year, 5-node baseline)(USD thousands) | $180,000-$250,000 | $120,000-$180,000 | |
| Starting Monthly Cost(USD) | $1,500-$4,000 | $1,500-$4,000 | |
| Setup Time(minutes) | 3-7 days | 3-7 days | |
| Query Performance (TPC-DS)(seconds) | 18-25 | 18-25 | |
| ML/AI Integration Score(out of 10) | 9/10 | 9/10 | |
| Global Enterprise Customers(count (2026)) | 6,500+ | 6,500+ | |
| 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) | |
| Setup Time to Production(hours) | 1-2 weeks | 1-2 weeks | |
| 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(services) | 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) | |
| 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 | |
| SQL Query Performance (1TB dataset)(seconds) | 8-15 seconds | 8-15 seconds | |
| Base Monthly Cost (minimum)(USD) | $500+ | $500+ | |
| Data Format Support(format types) | Any format (structured, unstructured, images, video) | Any format (structured, unstructured, images, video) | |
| Concurrent Users Support(users) | Unlimited (serverless SQL analytics) | Unlimited (serverless SQL analytics) | |
| Data Warehouse Setup Time(minutes) | 15-30 minutes | 15-30 minutes | |
| Global Market Share (2024)(percent) | 18% of lakehouse market | 18% of lakehouse market | |
| ML Model Training Cost Efficiency(relative cost index) | 1.0x baseline (integrated ML platform) | 1.0x baseline (integrated ML platform) | |
| Starting Monthly Cost (10GB active data)(USD) | $650 | $650 | |
| SQL Query Performance (TPC-DS Benchmark)(seconds) | 45 | 45 | |
| BI Tool Native Connectors(count) | 65 | 65 | |
| Customer Satisfaction Rating (G2 2025)(percent) | 82% | 82% | |
| Setup Complexity (1-10 scale)(score) | 7 | 7 |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
AH
0Apache HadoopDatabricks leads1 tie
Apache Hadoop
Attribute
Databricks
- Self-managed on-premises or IaaSDeployment ModelFully managed SaaS (AWS, Azure, GCP)(winner)
- MapReduce (batch-oriented, 2-10x slower than Spark)Processing EngineApache Spark (in-memory, 100x faster for iterative workloads)(winner)
- 4-8 weeks for production clusterSetup Time15 minutes to launch workspace(winner)
- $180,000-$250,000 (hardware + ops staff)Total Cost of Ownership (per 100TB/year)$120,000-$180,000 (compute + storage + licensing)(winner)
- Minimal (requires third-party tools like Apache Ranger)Data Governance FeaturesNative (Unity Catalog with row/column-level access control)(winner)
- Limited (Hive queries often >30 seconds on cold data)Real-time SQL Query SupportNative (sub-second queries with Photon engine)(winner)
- Large open-source community; enterprise support via Cloudera/HortonworksCommunity & Enterprise SupportVendor-backed (Databricks Inc.) with dedicated support SLAs
- Deployment Model
Apache Hadoop
Self-managed on-premises or IaaS
Databricks
Fully managed SaaS (AWS, Azure, GCP)(winner)
- Processing Engine
Apache Hadoop
MapReduce (batch-oriented, 2-10x slower than Spark)
Databricks
Apache Spark (in-memory, 100x faster for iterative workloads)(winner)
- Setup Time
Apache Hadoop
4-8 weeks for production cluster
Databricks
15 minutes to launch workspace(winner)
- Total Cost of Ownership (per 100TB/year)
Apache Hadoop
$180,000-$250,000 (hardware + ops staff)
Databricks
$120,000-$180,000 (compute + storage + licensing)(winner)
- Data Governance Features
Apache Hadoop
Minimal (requires third-party tools like Apache Ranger)
Databricks
Native (Unity Catalog with row/column-level access control)(winner)
- Real-time SQL Query Support
Apache Hadoop
Limited (Hive queries often >30 seconds on cold data)
Databricks
Native (sub-second queries with Photon engine)(winner)
- Community & Enterprise Support
Apache Hadoop
Large open-source community; enterprise support via Cloudera/Hortonworks
Databricks
Vendor-backed (Databricks Inc.) with dedicated support SLAs
Full Comparison
| Attribute | Apache Hadoop | Databricks |
|---|---|---|
| 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 3 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 $120,000-$180,000 Initial Licensing Cost(USD) $2,000-$15,000/month — | ||
| Setup Time(minutes) | 28-84 days | — |
| Setup Complexity (1=Simple, 10=Complex)(scale) | 7/10 | — |
| Required IT Staff (FTE)(people) | 5-10 FTE | — |
| Minimum Cluster Size(nodes) | 3-5 nodes | — |
| Required Team Skills (FTE equivalents for operations)(FTE) | 2-3 dedicated engineers | 0.25 (minimal management)(winner) |
| Admin/DevOps Time Required (Monthly)(hours) | 40-80 hours (patching, monitoring, scaling) | — |
| Team Expertise Required(months to proficiency) | 6-12 months | — |
Show 4 more attributesInitial Deployment Time(weeks) 4-8 weeks 0.25 weeks (15 minutes) Cluster Auto-scaling Capability(supported) Manual (requires YARN configuration) Automatic (5-30 min provisioning) Cluster Management Required(hours/month) Minimal (<5 hours/month) — Infrastructure Management Required(null) Manual cluster setup and scaling — | ||
| 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) | 10-100x faster(winner) |
Show 19 more attributesQuery Latency (median, standard ETL workload)(seconds) 45-120 seconds 3.5-8 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) 50-100x faster (Spark + Photon) SQL Query Latency (100GB dataset)(seconds) 15-45 seconds (Hive) 0.5-3 seconds (Photon) 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) — 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) — 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) — | ||
| 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) | — |
| Concurrent Users Support(users) | Unlimited (serverless SQL analytics) | — |
| Availability SLA(percent uptime) | 95-99% (cluster-dependent) | — |
| Fault Tolerance Mechanism | Task re-execution + HDFS replication | — |
| Fault Tolerance Method(mechanism) | Replication (3x copies) | — |
| Enterprise SLA Uptime(percent) | 99.9% | — |
| Memory Usage per Node(GB) | 8-32 GB | — |
| Supported Languages(count) | 2 (Java, Scala) | — |
| GitHub Stars (2025)(stars) | 12.4K | — |
| Community Size (GitHub Stars)(stars) | 13,500 stars (hadoop/hadoop)(winner) | 8,200 stars (databricks/databricks-cli) |
| Community Size(users) | 8,000+ questions | — |
| Optimal Dataset Size(GB minimum) | 100+ GB batches | — |
| Initial Setup Time to Production(weeks) | 8-12 weeks | 1-2 weeks(winner) |
| On-Premises Deployment Option(supported) | Yes (full control) | No (cloud-only) |
| Setup Complexity (1-10 scale)(score) | 7 | — |
| Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) | $2,500-5,000 (infrastructure only) | $550-850(winner) |
| 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 | — |
Show 6 more attributesCompute 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) — Starting Monthly Cost (10TB workload)(USD) $3,500-$5,000 — Base Monthly Cost (minimum)(USD) $500+ — Starting Monthly Cost (10GB active data)(USD) $650 — | ||
| SQL Query Standards Compliance(% ANSI SQL support) | Hive SQL (65% ANSI) | Full ANSI SQL (100%)(winner) |
| Built-in Collaboration Tools (notebooks, dashboards, repos)(count) | 0 (requires third-party) | Notebooks, Dashboards, SQL Editor, Repos, MLflow(winner) |
| Real-time Streaming Capability(latency (ms)) | Not supported | — |
| Data Sharing Standard(technology) | Delta Sharing (open standard) | — |
| Native ML/AI Capabilities | Native (MLflow, AutoML, Feature Store) | — |
| Setup Time to First Query(minutes) | 30-90 days (cluster + network + security) | — |
| Minimum Learning Curve (months for competency)(months) | 2-3 months | — |
| Data Warehouse Setup Time(minutes) | 15-30 minutes | — |
| Maximum Unstructured Data Support(% of typical use cases) | 90% (native HDFS support for any file type) | — |
| Supported Programming Languages(count) | Java, Scala | — |
| Data Residency Control | Complete (on-premises or self-managed cloud) | — |
| Memory Requirement (Per Node)(GB) | 4-8 GB | — |
| Supported Cloud Platforms | AWS, Azure, GCP | — |
| Cloud Providers(count) | 3 (AWS, Azure, GCP) | — |
| Global Region Availability(regions) | 60+ (via partner clouds) | — |
| First Release(year) | 2011 | — |
| Market Adoption by Fortune 500(percent) | 35% | — |
| Initial Setup Time(minutes) | 4-12 weeks | — |
| Setup Time(minutes) | 3-7 days | — |
| Customer Satisfaction Rating (G2 2025)(percent) | 82% | — |
| Supported Processing Models(count) | 4+ (batch, streaming, graph, ML) | — |
| Vendor Lock-in Risk(risk level) | Low (portable open-source) | — |
| Supported Data Formats(types) | All formats (Delta, Parquet, Images, Videos, Audio) | — |
| Multi-Cloud Support(cloud providers) | AWS, Azure, GCP | — |
| Data Format Support(format types) | Any format (structured, unstructured, images, video) | — |
| Native Row/Column-Level Access Control(supported) | No (requires Ranger) | Yes (Unity Catalog native) |
| Data Governance (Unity Catalog equivalent)(null) | Unity Catalog with lineage, tags, access control | — |
| Collaborative Notebooks with Version Control(native support) | No (requires Jupyter/Git separately) | Yes (built-in with Git integration) |
| Real-Time Notebook Collaboration Users(concurrent users) | Unlimited simultaneous editing | — |
| Users Per Collaborative Project(concurrent users) | Unlimited with real-time sync | — |
| 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)(percent) | 18% of lakehouse market | — |
| Multi-Language Support(languages) | SQL, Python, Scala, R, Java | — |
| Pre-built AutoML Models(models) | 12+ model families via AutoML | — |
| 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 | — |
| Setup Time to Production(hours) | 1-2 weeks | — |
| Time to Deploy (Basic Setup)(days) | 3-7 days | — |
| Typical Enterprise Migration Time(months) | 3-6 months (focused data/AI projects) | — |
| 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 | — |
| SQL Standard Compliance Level(null) | ANSI SQL with Spark extensions | — |
| Supported Data Connectors(count) | 15+ native connectors | — |
| 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 | — |
| Deployment Options | Cloud-only (3 regions) | — |
| Available Services(services) | 25+ integrated | — |
| 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+ | — |
| 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 | — |
| Supported Data Types | Structured, semi-structured, unstructured | — |
| Total Service Offerings(services) | ~15 core data/AI services | — |
| Enterprise Service Count(services) | 50+ (data/AI focused) | — |
| Microsoft Ecosystem Integration(native integrations) | Limited (Power BI via connector only) | — |
| Data Governance Granularity(access level) | Column, row, and table-level with tags | — |
| ACID Transaction Support(boolean) | Native (Delta Lake) | — |
| ML Model Training Cost Efficiency(relative cost index) | 1.0x baseline (integrated ML platform) | — |
| Data Format Lock-in Risk | Low (open Delta/Iceberg formats) | — |
AApache Hadoop
DatabricksTotal 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 3 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
$120,000-$180,000
Initial Licensing Cost(USD)
$2,000-$15,000/month
—
Setup Time(minutes)
28-84 days
—
Setup Complexity (1=Simple, 10=Complex)(scale)
7/10
—
Required IT Staff (FTE)(people)
5-10 FTE
—
Minimum Cluster Size(nodes)
3-5 nodes
—
Required Team Skills (FTE equivalents for operations)(FTE)
2-3 dedicated engineers
0.25 (minimal management)
Admin/DevOps Time Required (Monthly)(hours)
40-80 hours (patching, monitoring, scaling)
—
Team Expertise Required(months to proficiency)
6-12 months
—
Show 4 more attributes
Initial Deployment Time(weeks)
4-8 weeks
0.25 weeks (15 minutes)
Cluster Auto-scaling Capability(supported)
Manual (requires YARN configuration)
Automatic (5-30 min provisioning)
Cluster Management Required(hours/month)
Minimal (<5 hours/month)
—
Infrastructure Management Required(null)
Manual cluster setup and scaling
—
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)
10-100x faster
Show 19 more attributes
Query Latency (median, standard ETL workload)(seconds)
45-120 seconds
3.5-8 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)
50-100x faster (Spark + Photon)
SQL Query Latency (100GB dataset)(seconds)
15-45 seconds (Hive)
0.5-3 seconds (Photon)
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)
—
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)
—
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)
—
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)
—
Concurrent Users Support(users)
Unlimited (serverless SQL analytics)
—
Availability SLA(percent uptime)
95-99% (cluster-dependent)
—
Fault Tolerance Mechanism
Task re-execution + HDFS replication
—
Fault Tolerance Method(mechanism)
Replication (3x copies)
—
Enterprise SLA Uptime(percent)
99.9%
—
Memory Usage per Node(GB)
8-32 GB
—
Supported Languages(count)
2 (Java, Scala)
—
GitHub Stars (2025)(stars)
12.4K
—
Community Size (GitHub Stars)(stars)
13,500 stars (hadoop/hadoop)
8,200 stars (databricks/databricks-cli)
Community Size(users)
8,000+ questions
—
Optimal Dataset Size(GB minimum)
100+ GB batches
—
Initial Setup Time to Production(weeks)
8-12 weeks
1-2 weeks
On-Premises Deployment Option(supported)
Yes (full control)
No (cloud-only)
Setup Complexity (1-10 scale)(score)
7
—
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD)
$2,500-5,000 (infrastructure only)
$550-850
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
—
Show 6 more attributes
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)
—
Starting Monthly Cost (10TB workload)(USD)
$3,500-$5,000
—
Base Monthly Cost (minimum)(USD)
$500+
—
Starting Monthly Cost (10GB active data)(USD)
$650
—
SQL Query Standards Compliance(% ANSI SQL support)
Hive SQL (65% ANSI)
Full ANSI SQL (100%)
Built-in Collaboration Tools (notebooks, dashboards, repos)(count)
0 (requires third-party)
Notebooks, Dashboards, SQL Editor, Repos, MLflow
Real-time Streaming Capability(latency (ms))
Not supported
—
Data Sharing Standard(technology)
Delta Sharing (open standard)
—
Native ML/AI Capabilities
Native (MLflow, AutoML, Feature Store)
—
Setup Time to First Query(minutes)
30-90 days (cluster + network + security)
—
Minimum Learning Curve (months for competency)(months)
2-3 months
—
Data Warehouse Setup Time(minutes)
15-30 minutes
—
Maximum Unstructured Data Support(% of typical use cases)
90% (native HDFS support for any file type)
—
Supported Programming Languages(count)
Java, Scala
—
Data Residency Control
Complete (on-premises or self-managed cloud)
—
Memory Requirement (Per Node)(GB)
4-8 GB
—
Supported Cloud Platforms
AWS, Azure, GCP
—
Cloud Providers(count)
3 (AWS, Azure, GCP)
—
Global Region Availability(regions)
60+ (via partner clouds)
—
First Release(year)
2011
—
Market Adoption by Fortune 500(percent)
35%
—
Initial Setup Time(minutes)
4-12 weeks
—
Setup Time(minutes)
3-7 days
—
Customer Satisfaction Rating (G2 2025)(percent)
82%
—
Supported Processing Models(count)
4+ (batch, streaming, graph, ML)
—
Vendor Lock-in Risk(risk level)
Low (portable open-source)
—
Supported Data Formats(types)
All formats (Delta, Parquet, Images, Videos, Audio)
—
Multi-Cloud Support(cloud providers)
AWS, Azure, GCP
—
Data Format Support(format types)
Any format (structured, unstructured, images, video)
—
Native Row/Column-Level Access Control(supported)
No (requires Ranger)
Yes (Unity Catalog native)
Data Governance (Unity Catalog equivalent)(null)
Unity Catalog with lineage, tags, access control
—
Collaborative Notebooks with Version Control(native support)
No (requires Jupyter/Git separately)
Yes (built-in with Git integration)
Real-Time Notebook Collaboration Users(concurrent users)
Unlimited simultaneous editing
—
Users Per Collaborative Project(concurrent users)
Unlimited with real-time sync
—
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)(percent)
18% of lakehouse market
—
Multi-Language Support(languages)
SQL, Python, Scala, R, Java
—
Pre-built AutoML Models(models)
12+ model families via AutoML
—
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
—
Setup Time to Production(hours)
1-2 weeks
—
Time to Deploy (Basic Setup)(days)
3-7 days
—
Typical Enterprise Migration Time(months)
3-6 months (focused data/AI projects)
—
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
—
SQL Standard Compliance Level(null)
ANSI SQL with Spark extensions
—
Supported Data Connectors(count)
15+ native connectors
—
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
—
Deployment Options
Cloud-only (3 regions)
—
Available Services(services)
25+ integrated
—
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+
—
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
—
Supported Data Types
Structured, semi-structured, unstructured
—
Total Service Offerings(services)
~15 core data/AI services
—
Enterprise Service Count(services)
50+ (data/AI focused)
—
Microsoft Ecosystem Integration(native integrations)
Limited (Power BI via connector only)
—
Data Governance Granularity(access level)
Column, row, and table-level with tags
—
ACID Transaction Support(boolean)
Native (Delta Lake)
—
ML Model Training Cost Efficiency(relative cost index)
1.0x baseline (integrated ML platform)
—
Data Format Lock-in Risk
Low (open Delta/Iceberg formats)
—
Pros & Cons
11 pros·6 cons across both
AH
AH
Apache Hadoop
+5-3
Pros
- 100% open-source with no licensing fees after infrastructure investment
- Complete control over data placement and cluster configuration
- Mature ecosystem (10+ years production usage at scale)
- Works on commodity hardware (cost-effective at massive scale >1000 nodes)
- HDFS provides fault tolerance across 1000+ nodes with replication factor 3
Cons
- Requires 4-8 weeks of DevOps/Hadoop expertise to deploy and tune production clusters
- MapReduce is 2-10x slower than Spark for iterative ML workloads
- Complex security model requires Apache Ranger/Kerberos integration for governance
Databricks
+6-3
Pros
- Fully managed infrastructure—no cluster management or patching required
- Apache Spark engine processes most workloads 10-100x faster than Hadoop MapReduce
- Unified analytics workspace combines SQL, Python, R, Scala in collaborative notebooks
- Native Unity Catalog provides row/column-level access control and data lineage tracking
- Built-in MLflow for ML model versioning, tracking, and deployment across teams
- Auto-scaling compute (5-30 minute provisioning) matches workload demand automatically
Cons
- $0.25-$0.50 per DBU/hour adds $150,000-$300,000+ annually for moderate workloads (vs. fixed Hadoop capex)
- Lock-in to cloud provider (AWS, Azure, or GCP)—data portability requires migration effort
- Smaller open-source ecosystem compared to pure Hadoop; less third-party tool compatibility
Frequently Asked Questions
5 questions
Hadoop remains relevant for organizations with existing multi-thousand-node on-premises clusters and strict data residency requirements, but adoption of new Hadoop clusters has declined 75% since 2018 as Spark/Databricks became standard. Most enterprises now migrate workloads to Spark-based platforms rather than invest in new Hadoop infrastructure.
Resources & Learn More
Curated sources to dive deeper
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Wikipedia
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