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.
Hadoop vs Databricks — Side-by-side comparison on A Versus B
Open-source distributed computing framework using MapReduce and HDFS for batch processing
Managed cloud analytics platform built on Apache Spark with native collaboration, MLOps, and governance
Deployment Model. Processing Engine. Setup Time. Total Cost of Ownership (per 100TB/year). Data Governance Features. Real-time SQL Query Support. Community & Enterprise Support
Choose 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.
Total Cost of Ownership (5 years, 100TB)
Apache Hadoop: 28-84 days | Databricks: N/A
Apache Hadoop: 5-10 FTE | Databricks: N/A
Apache Hadoop: 20-50 ms | Databricks: N/A
Apache Hadoop: Limited by cluster (typically 10-100 PB) | Databricks: N/A
Apache Hadoop: 24-72 hours | Databricks: N/A
Apache Hadoop: 95-99% (cluster-dependent) | Databricks: N/A
Apache Hadoop: $12,500-$25,000 | Databricks: N/A
Apache Hadoop: 180-3600 seconds | Databricks: N/A
Apache Hadoop: 100K-500K | Databricks: N/A
Apache Hadoop: 8-32 GB | Databricks: N/A
Apache Hadoop: 3-5 nodes | Databricks: N/A
Apache Hadoop: 2 (Java, Scala) | Databricks: N/A
Apache Hadoop: Task re-execution + HDFS replication | Databricks: N/A
Apache Hadoop: 100+ GB batches | Databricks: N/A
Apache Hadoop: 300-600 seconds (disk-based) | Databricks: N/A
Apache Hadoop: 8-12 weeks | Databricks: 1-2 weeks
Apache Hadoop: 1x (baseline) | Databricks: 10-100x faster
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
Apache Hadoop: $2,500-5,000 (infrastructure only) | Databricks: $550-850
Required Team Skills (FTE equivalents for operations)
Apache Hadoop: 2-3 dedicated engineers | Databricks: 0.25 (minimal management)
Apache Hadoop: Hive SQL (65% ANSI) | Databricks: Full ANSI SQL (100%)
Query Latency (median, standard ETL workload)
Apache Hadoop: 45-120 seconds | Databricks: 3.5-8 seconds
Built-in Collaboration Tools (notebooks, dashboards, repos)
Apache Hadoop: 0 (requires third-party) | Databricks: Notebooks, Dashboards, SQL Editor, Repos, MLflow
Apache Hadoop: 13,500 stars (hadoop/hadoop) | Databricks: 8,200 stars (databricks/databricks-cli)
Apache Hadoop: 10-30 seconds (with Spark) | Databricks: N/A
Infrastructure Cost (Annual, 50TB dataset)
Apache Hadoop: $150,000-$250,000 | Databricks: N/A
Infrastructure Cost (Annual, 500TB dataset)
Apache Hadoop: $200,000-$400,000 | Databricks: N/A
Apache Hadoop: 30-90 days (cluster + network + security) | Databricks: N/A
Apache Hadoop: 90% (native HDFS support for any file type) | Databricks: N/A
Apache Hadoop: 40-80 hours (patching, monitoring, scaling) | Databricks: N/A
Apache Hadoop: Complete (on-premises or self-managed cloud) | Databricks: N/A
Apache Hadoop: 10,000+ (custom hardware limits) | Databricks: N/A
Apache Hadoop: 300-600 seconds | Databricks: N/A
Apache Hadoop: Java, Scala | Databricks: N/A
Apache Hadoop: Not supported | Databricks: N/A
Apache Hadoop: $180,000-250,000 | Databricks: N/A
Apache Hadoop: Replication (3x copies) | Databricks: N/A
Apache Hadoop: 4-12 weeks | Databricks: N/A
Apache Hadoop: 120-300 seconds | Databricks: N/A
Apache Hadoop: $150,000-$400,000 | Databricks: N/A
Apache Hadoop: 6-12 months | Databricks: N/A
Apache Hadoop: 4+ (batch, streaming, graph, ML) | Databricks: N/A
Apache Hadoop: Unlimited (cluster-dependent) | Databricks: N/A
Apache Hadoop: Low (portable open-source) | Databricks: N/A
Apache Hadoop: 4-8 weeks | Databricks: 0.25 weeks (15 minutes)
Apache Hadoop: 1x (MapReduce baseline) | Databricks: 50-100x faster (Spark + Photon)
Apache Hadoop: 15-45 seconds (Hive) | Databricks: 0.5-3 seconds (Photon)
Annual Cost (100TB/year, 5-node baseline)
Apache Hadoop: No (requires Ranger) | Databricks: Yes (Unity Catalog native)
Apache Hadoop: Manual (requires YARN configuration) | Databricks: Automatic (5-30 min provisioning)
Collaborative Notebooks with Version Control
Apache Hadoop: No (requires Jupyter/Git separately) | Databricks: Yes (built-in with Git integration)
Apache Hadoop: Yes (full control) | Databricks: No (cloud-only)
Apache Hadoop: N/A | Databricks: $1,500-$4,000
Apache Hadoop: N/A | Databricks: 3-7 days
Apache Hadoop: N/A | Databricks: All formats (Delta, Parquet, Images, Videos, Audio)
Apache Hadoop: N/A | Databricks: Delta Sharing (open standard)
Apache Hadoop: N/A | Databricks: SQL, Python, Scala, R, Java
Apache Hadoop: N/A | Databricks: AWS, Azure, GCP
Apache Hadoop: N/A | Databricks: $0.30 (1 DBU compute)
Apache Hadoop: N/A | Databricks: 12+ model families via AutoML
Apache Hadoop: N/A | Databricks: Unlimited simultaneous editing
Apache Hadoop: N/A | Databricks: 15+ (S3, RDS, Kinesis)
Apache Hadoop: N/A | Databricks: Native Delta Lake engine
Apache Hadoop: N/A | Databricks: Up to 70% savings
SQL Query Performance (sample 1TB table)
Apache Hadoop: N/A | Databricks: 8-15 (native optimizations)
Apache Hadoop: N/A | Databricks: $2,000-$15,000/month
Apache Hadoop: N/A | Databricks: 1-2 weeks
Apache Hadoop: N/A | Databricks: Minimal (<5 hours/month)
Apache Hadoop: N/A | Databricks: Unlimited with real-time sync
Apache Hadoop: N/A | Databricks: 6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation)
Apache Hadoop: N/A | Databricks: All Spark formats + native Delta Lake optimization
Apache Hadoop: N/A | Databricks: 8,000+ questions
Apache Hadoop: N/A | Databricks: $500-2,000
Apache Hadoop: N/A | Databricks: ANSI SQL with Spark extensions
Apache Hadoop: N/A | Databricks: 15+ native connectors
Apache Hadoop: N/A | Databricks: MLflow, Feature Store, AutoML included
Apache Hadoop: N/A | Databricks: Yes, into Delta Lake
Apache Hadoop: N/A | Databricks: 1-5 seconds (on cached data)
Apache Hadoop: N/A | Databricks: $600-900
Apache Hadoop: N/A | Databricks: 10x (Photon engine)
Apache Hadoop: N/A | Databricks: 25+ integrated
BigQuery/Equivalent Query Speed (1TB dataset)
Apache Hadoop: N/A | Databricks: 15-30 sec (via Databricks SQL)
Apache Hadoop: N/A | Databricks: 30,000+
Apache Hadoop: N/A | Databricks: MLflow + Databricks Intelligence Engine (built-in)
Apache Hadoop: N/A | Databricks: Native Delta Lake with ACID, time travel, schema evolution
Apache Hadoop: N/A | Databricks: 10,000+
Apache Hadoop: N/A | Databricks: Cloud-only (3 regions)
Apache Hadoop: N/A | Databricks: Native MLflow Feature Store included
Apache Hadoop: N/A | Databricks: 40-100 ms
Apache Hadoop: N/A | Databricks: $0.40-0.50
Data Governance (Unity Catalog equivalent)
Apache Hadoop: N/A | Databricks: Unity Catalog with lineage, tags, access control
Apache Hadoop: N/A | Databricks: 5-15 seconds
Apache Hadoop: N/A | Databricks: Structured, semi-structured, unstructured
Apache Hadoop: N/A | Databricks: 3 (AWS, Azure, GCP)
Apache Hadoop: N/A | Databricks: Manual cluster setup and scaling
Apache Hadoop: N/A | Databricks: MLflow, Spark MLlib, TensorFlow, PyTorch
Minimum Learning Curve (months for competency)
Apache Hadoop: N/A | Databricks: 2-3 months
Starting Monthly Cost (1 TB storage + compute)
Apache Hadoop: N/A | Databricks: $400-800 (variable compute)
Apache Hadoop: N/A | Databricks: 10-100x faster (Photon engine)
Apache Hadoop: N/A | Databricks: ~15 core data/AI services
Apache Hadoop: N/A | Databricks: $0.50-$2.50 (depends on cloud provider)
Apache Hadoop: N/A | Databricks: MLflow, Feature Store, Model Registry
Apache Hadoop: N/A | Databricks: Limited (Power BI via connector only)
Apache Hadoop: N/A | Databricks: Unity Catalog, lineage, access control, Delta Lake
Apache Hadoop: N/A | Databricks: 3-6 months (focused data/AI projects)
Apache Hadoop: N/A | Databricks: 285 seconds
Apache Hadoop: N/A | Databricks: 3-5x faster (Photon engine)
Apache Hadoop: N/A | Databricks: 8 frameworks (via MLflow ecosystem)
Apache Hadoop: N/A | Databricks: Column, row, and table-level with tags
Apache Hadoop: N/A | Databricks: 60+ (via partner clouds)
Apache Hadoop: N/A | Databricks: 50+ (data/AI focused)
Apache Hadoop: N/A | Databricks: $3,500-$5,000
Apache Hadoop: N/A | Databricks: Native (Delta Lake)
Apache Hadoop: N/A | Databricks: 8-15 seconds
Apache Hadoop: N/A | Databricks: Native (MLflow, AutoML, Feature Store)
Apache Hadoop: N/A | Databricks: Any format (structured, unstructured, images, video)
Apache Hadoop: N/A | Databricks: Unlimited (serverless SQL analytics)
Apache Hadoop: N/A | Databricks: 15-30 minutes
Apache Hadoop: N/A | Databricks: 18% of lakehouse market
Apache Hadoop: N/A | Databricks: 1.0x baseline (integrated ML platform)
Starting Monthly Cost (10GB active data)
SQL Query Performance (TPC-DS Benchmark)
Apache Hadoop: N/A | Databricks: MLflow + Spark ML
Apache Hadoop: N/A | Databricks: Low (open Delta/Iceberg formats)
Maximum Concurrent Queries Per Warehouse
Apache Hadoop: N/A | Databricks: Unlimited (Spark clusters)
How to choose between Apache Hadoop and Databricks
Hadoop vs Databricks — Structured Comparison Data
Machine-readable comparison dataset for Hadoop vs Databricks. Contains 141 attribute comparisons, 5 FAQ pairs.
Hadoop vs Databricks — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Hadoop vs Databricks.
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.
Yes. Spark can run on top of Hadoop's YARN resource manager, but this is increasingly rare. Most Spark deployments run on Kubernetes or managed platforms like Databricks. Running Spark on Hadoop YARN still requires Hadoop cluster operations expertise and doesn't eliminate Hadoop's operational burden.
What's the actual cost difference between Hadoop and Databricks?
For a typical 100TB/year workload: Hadoop costs $180K-$250K annually (hardware, networking, 3-5 FTE ops staff at $150K-$200K each). Databricks costs $120K-$180K (compute + storage + $0.30/DBU average). Databricks breaks even in year 1 through reduced ops overhead (1-2 FTE instead of 3-5), then saves 30-40% annually in subsequent years.
Does Databricks replace my data warehouse?
Partially. Databricks (with Delta Lake) handles both warehouse and lakehouse workloads, but specialized data warehouses (Snowflake, Redshift) still excel at structured reporting with pure SQL. Databricks is ideal if you need both BI/analytics AND ML/AI in one platform; use a data warehouse if you only need traditional reporting.
Can I migrate from Hadoop to Databricks without rebuilding pipelines?
Partially. MapReduce jobs must be rewritten as Spark code (Python/SQL), which takes 2-4 weeks per pipeline. HDFS data can migrate to cloud object storage (S3/Azure Blob) in 1-2 weeks. Most organizations rewrite 30-50% of legacy Hadoop logic during migration to take advantage of Spark's 10-50x performance gains.
How to Choose Between Apache Hadoop and Databricks
Deployment Model: Apache Hadoop has Self-managed on-premises or IaaS, while Databricks has Fully managed SaaS (AWS, Azure, GCP).
Processing Engine: Apache Hadoop has MapReduce (batch-oriented, 2-10x slower than Spark), while Databricks has Apache Spark (in-memory, 100x faster for iterative workloads).
Setup Time: Apache Hadoop has 4-8 weeks for production cluster, while Databricks has 15 minutes to launch workspace.
Consider: Total Cost of Ownership (per 100TB/year)
Total Cost of Ownership (per 100TB/year): Apache Hadoop has $180,000-$250,000 (hardware + ops staff), while Databricks has $120,000-$180,000 (compute + storage + licensing).
Data Governance Features: Apache Hadoop has Minimal (requires third-party tools like Apache Ranger), while Databricks has Native (Unity Catalog with row/column-level access control).
Real-time SQL Query Support: Apache Hadoop has Limited (Hive queries often >30 seconds on cold data), while Databricks has Native (sub-second queries with Photon engine).
Consider: Community & Enterprise Support
Community & Enterprise Support: Apache Hadoop has Large open-source community; enterprise support via Cloudera/Hortonworks, while Databricks has Vendor-backed (Databricks Inc.) with dedicated support SLAs.