Hadoop is a batch processing framework optimized for large-scale data storage and processing with high latency, while Apache Flink is a stream processing engine designed for real-time data processing with sub-second latency. Flink processes continuous data streams natively, whereas Hadoop processes static data in batches.
Hadoop vs Apache Flink — Side-by-side comparison on A Versus B
Distributed batch processing framework for large-scale data storage and analysis using MapReduce paradigm.
Modern distributed stream processing framework with event-time semantics and exactly-once guarantees.
Processing Model. Latency. State Management. Memory Efficiency. Ecosystem Maturity. Community Size. Use Case Fit
Choose Hadoop if you need robust batch processing for large historical datasets, have existing investments in the ecosystem, and can tolerate latency of minutes to hours. Choose Flink if you require real-time or near-real-time stream processing, need complex stateful computations, or want unified batch and streaming capabilities with lower resource overhead.
Total Cost of Ownership (5 years, 100TB)
Apache Hadoop: $1,200,000-$1,800,000 | Apache Flink: N/A
Apache Hadoop: 28-84 days | Apache Flink: N/A
Apache Hadoop: 5-10 FTE | Apache Flink: N/A
Apache Hadoop: 20-50 ms | Apache Flink: N/A
Apache Hadoop: Limited by cluster (typically 10-100 PB) | Apache Flink: N/A
Apache Hadoop: 24-72 hours | Apache Flink: N/A
Apache Hadoop: 95-99% (cluster-dependent) | Apache Flink: N/A
Apache Hadoop: $12,500-$25,000 | Apache Flink: N/A
Apache Hadoop: 300-1800 seconds (5-30 minutes) | Apache Flink: 100-500 milliseconds
Apache Hadoop: 100K-500K | Apache Flink: 1M-10M
Apache Hadoop: 8-32 GB | Apache Flink: 4-16 GB
Apache Hadoop: 2 (Java, Scala) | Apache Flink: 4 (Java, Scala, Python, SQL)
Apache Hadoop: Task re-execution + HDFS replication | Apache Flink: Distributed snapshots + checkpointing
Apache Hadoop: 12.4K | Apache Flink: 23.8K
Apache Hadoop: 100+ GB batches | Apache Flink: Continuous streams (any size)
Apache Hadoop: 300-600 seconds (disk-based) | Apache Flink: N/A
Apache Hadoop: 8-12 weeks | Apache Flink: N/A
Apache Hadoop: 1x (baseline) | Apache Flink: N/A
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
Apache Hadoop: $2,500-5,000 (infrastructure only) | Apache Flink: N/A
Required Team Skills (FTE equivalents for operations)
Apache Hadoop: 2-3 dedicated engineers | Apache Flink: N/A
Apache Hadoop: Hive SQL (65% ANSI) | Apache Flink: N/A
Query Latency (median, standard ETL workload)
Apache Hadoop: 45-120 seconds | Apache Flink: N/A
Built-in Collaboration Tools (notebooks, dashboards, repos)
Apache Hadoop: 0 (requires third-party) | Apache Flink: N/A
Apache Hadoop: 10-30 seconds (with Spark) | Apache Flink: N/A
Infrastructure Cost (Annual, 50TB dataset)
Apache Hadoop: $150,000-$250,000 | Apache Flink: N/A
Infrastructure Cost (Annual, 500TB dataset)
Apache Hadoop: $200,000-$400,000 | Apache Flink: N/A
Apache Hadoop: 30-90 days (cluster + network + security) | Apache Flink: N/A
Apache Hadoop: 90% (native HDFS support for any file type) | Apache Flink: N/A
Apache Hadoop: 40-80 hours (patching, monitoring, scaling) | Apache Flink: N/A
Apache Hadoop: Complete (on-premises or self-managed cloud) | Apache Flink: N/A
Apache Hadoop: 10,000+ (custom hardware limits) | Apache Flink: N/A
Apache Hadoop: 300-600 seconds | Apache Flink: N/A
Apache Hadoop: 4-8 GB | Apache Flink: N/A
Apache Hadoop: 5 languages (Java, Python, Scala, C++, Ruby) | Apache Flink: 4 languages (Java, Scala, Python, SQL)
Apache Hadoop: Not supported | Apache Flink: N/A
Apache Hadoop: $180,000-250,000 | Apache Flink: N/A
Apache Hadoop: Replication (3x copies) | Apache Flink: N/A
Apache Hadoop: 3 weeks | Apache Flink: N/A
Apache Hadoop: 120-300 seconds | Apache Flink: N/A
Apache Hadoop: $150,000-$400,000 | Apache Flink: N/A
Apache Hadoop: 6-12 months | Apache Flink: N/A
Apache Hadoop: 4+ (batch, streaming, graph, ML) | Apache Flink: N/A
Apache Hadoop: Unlimited (cluster-dependent) | Apache Flink: N/A
Apache Hadoop: Low (portable open-source) | Apache Flink: N/A
Apache Hadoop: 4-8 weeks | Apache Flink: N/A
Apache Hadoop: 1x (MapReduce baseline) | Apache Flink: N/A
Apache Hadoop: 15-45 seconds (Hive) | Apache Flink: N/A
Annual Cost (100TB/year, 5-node baseline)
Apache Hadoop: $180,000-$250,000 | Apache Flink: N/A
Apache Hadoop: No (requires Ranger) | Apache Flink: N/A
Apache Hadoop: Manual (requires YARN configuration) | Apache Flink: N/A
Collaborative Notebooks with Version Control
Apache Hadoop: No (requires Jupyter/Git separately) | Apache Flink: N/A
Apache Hadoop: Yes (full control) | Apache Flink: N/A
Apache Hadoop: $150,000-$300,000 | Apache Flink: N/A
Apache Hadoop: 4-8 (DevOps, engineers, admins) | Apache Flink: N/A
Apache Hadoop: 50-100 per cluster | Apache Flink: N/A
Apache Hadoop: $12-20 | Apache Flink: N/A
Apache Hadoop: 40-60% reduction in network I/O | Apache Flink: N/A
Apache Hadoop: 5 (full MapReduce/Spark) | Apache Flink: N/A
Query Performance (10TB TPC-DS benchmark)
Apache Hadoop: 95 seconds | Apache Flink: N/A
Apache Hadoop: $120,000 | Apache Flink: N/A
Apache Hadoop: Tightly coupled (scale together) | Apache Flink: N/A
Apache Hadoop: Hive SQL (SQL92, limited optimizations) | Apache Flink: N/A
Apache Hadoop: Manual snapshots only | Apache Flink: N/A
Apache Hadoop: 3-5 engineers | Apache Flink: N/A
Apache Hadoop: 14,200 stars | Apache Flink: N/A
Apache Hadoop: 1M-10M per node | Apache Flink: 1M-100M per node
Apache Hadoop: 500-2000 MB | Apache Flink: 100-800 MB
Apache Hadoop: 15 years (since 2006) | Apache Flink: 10 years (since 2014)
Apache Hadoop: 14,500+ stars | Apache Flink: 23,500+ stars
Apache Hadoop: External solutions required (HBase, etc.) | Apache Flink: RocksDB (100GB+), HashMap, External KV stores
Apache Hadoop: 3 nodes recommended | Apache Flink: 1-2 nodes viable
Apache Hadoop: N/A | Apache Flink: 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints)
Time to First Correct Result (learning curve)
Apache Hadoop: N/A | Apache Flink: Yes (core feature)
Apache Hadoop: N/A | Apache Flink: 250,000
Apache Hadoop: N/A | Apache Flink: 5-50ms
Apache Hadoop: N/A | Apache Flink: Exactly-once (native)
Apache Hadoop: N/A | Apache Flink: 50-100ms
Apache Hadoop: N/A | Apache Flink: Exactly-once
Apache Hadoop: N/A | Apache Flink: Event-time, processing-time, session windows, custom
Apache Hadoop: N/A | Apache Flink: Requires cluster with YARN/Kubernetes, moderate DevOps
Apache Hadoop: N/A | Apache Flink: 8-12 weeks (with Kubernetes ops experience)
Apache Hadoop: N/A | Apache Flink: 1024 MB
Apache Hadoop: N/A | Apache Flink: 10,000+
Apache Hadoop: N/A | Apache Flink: 4 (Java, Python, Scala, SQL)
Apache Hadoop: N/A | Apache Flink: 100-500 ms (tuning dependent)
Apache Hadoop: N/A | Apache Flink: Terabyte-scale (tested to 10+ TB)
Apache Hadoop: N/A | Apache Flink: 29,000+
Apache Hadoop: N/A | Apache Flink: 23,800+
Apache Hadoop: N/A | Apache Flink: ~1,850
Apache Hadoop: N/A | Apache Flink: 1.5-2.5 GB
Apache Hadoop: N/A | Apache Flink: Limited (requires external libraries)
Apache Hadoop: N/A | Apache Flink: 2-3 seconds (streaming) / 4-6 (batch)
Apache Hadoop: N/A | Apache Flink: 100,000-1,000,000 events/sec
Apache Hadoop: N/A | Apache Flink: PyFlink added in v1.11 (2020); improved in v1.14+
Apache Hadoop: N/A | Apache Flink: Exactly-once (configurable per checkpoint)
Apache Hadoop: N/A | Apache Flink: Basic REST API only (external UI required)
Apache Hadoop: N/A | Apache Flink: Java 11+
Apache Hadoop: N/A | Apache Flink: ~30 native connectors
Apache Hadoop: N/A | Apache Flink: ~200-400MB (optimized)
Apache Hadoop: N/A | Apache Flink: Full with watermarks, out-of-order handling, allowedLateness
Apache Hadoop: N/A | Apache Flink: ~1-2M events/sec
Apache Hadoop: N/A | Apache Flink: Separate APIs (DataStream vs Batch)
Apache Hadoop: N/A | Apache Flink: 2-4 GB
Apache Hadoop: N/A | Apache Flink: Memory, RocksDB, External (3 options)
Apache Hadoop: N/A | Apache Flink: 0 (Open source)
Apache Hadoop: N/A | Apache Flink: 10,400
Apache Hadoop: N/A | Apache Flink: 12 (since 2014)
Apache Hadoop: N/A | Apache Flink: Millions (1M+)
Apache Hadoop: N/A | Apache Flink: 1-10ms (true event streaming)
Apache Hadoop: N/A | Apache Flink: 6-8GB (efficient state management)
Apache Hadoop: N/A | Apache Flink: 1,800,000 (StackOverflow, job postings 2024)
Apache Hadoop: N/A | Apache Flink: 2,000+ (Flink SQL, state backends, CEP library)
Apache Hadoop: N/A | Apache Flink: 18% (Alibaba, Netflix, Uber, Lyft use cases)
Apache Hadoop: N/A | Apache Flink: 6-10 weeks (steeper learning curve, less documentation)
Apache Hadoop: N/A | Apache Flink: 15-30 lines (native session window API)
Apache Hadoop: N/A | Apache Flink: 100-500ms
Apache Hadoop: N/A | Apache Flink: 2,500
Apache Hadoop: N/A | Apache Flink: 15-20%
Apache Hadoop: N/A | Apache Flink: 256-512MB
Apache Hadoop: N/A | Apache Flink: 5 types (keyed, operator, broadcast, queryable, custom)
Apache Hadoop: N/A | Apache Flink: 8,000-12,000
Apache Hadoop: N/A | Apache Flink: 1-10ms (streaming native)
Apache Hadoop: N/A | Apache Flink: 7-9 (complex)
Apache Hadoop: N/A | Apache Flink: 2048-8192
Apache Hadoop: N/A | Apache Flink: Terabytes of distributed state native
Apache Hadoop: N/A | Apache Flink: 20-40 hours (learning + development)
Apache Hadoop: N/A | Apache Flink: 500 ms
Apache Hadoop: N/A | Apache Flink: Millions (100M+ with tuning)
Apache Hadoop: N/A | Apache Flink: 2 GB (standalone single node)
Apache Hadoop: N/A | Apache Flink: 1,000+
Apache Hadoop: N/A | Apache Flink: 15,000+
Time-to-Production (Simple Real-time Dashboard)
Apache Hadoop: N/A | Apache Flink: 3-4 weeks
Apache Hadoop: N/A | Apache Flink: 2-3 GB
Apache Hadoop: N/A | Apache Flink: 70% (subset with UDF limitations)
Apache Hadoop: N/A | Apache Flink: 120-150
Apache Hadoop: N/A | Apache Flink: 10,000,000
Apache Hadoop: N/A | Apache Flink: Native with distributed snapshots
Apache Hadoop: N/A | Apache Flink: Full support with watermarks and allowed lateness
Apache Hadoop: N/A | Apache Flink: 2000+
Apache Hadoop: N/A | Apache Flink: 60+ core parameters
How to choose between Apache Hadoop and Apache Flink
Hadoop vs Apache Flink — Structured Comparison Data
Machine-readable comparison dataset for Hadoop vs Apache Flink. Contains 164 attribute comparisons, 5 FAQ pairs.
Hadoop vs Apache Flink — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Hadoop vs Apache Flink.
Can Hadoop process streaming data in real-time?
Hadoop is fundamentally a batch processing framework and cannot natively process streaming data in real-time. While Hadoop can be combined with streaming tools like Kafka or Storm, the processing itself still occurs in batches (typically every few minutes). For true real-time streaming, Flink or Spark Streaming are better choices.
Can Flink replace Hadoop for large-scale batch processing?
Yes, Flink can handle large-scale batch processing and often does so more efficiently than Hadoop due to in-memory processing and lower latency. However, Hadoop remains superior for extremely large historical datasets (100+ TB) where cost per TB is critical, as HDFS provides unmatched data locality and fault tolerance at petabyte scale.
Flink has a steeper initial learning curve due to event time semantics and state management complexity. However, Flink's unified API is easier to maintain long-term compared to Hadoop's MapReduce paradigm. For deployment, Hadoop has more mature operational tooling and broader DevOps familiarity across the industry.
What are the cost differences between Hadoop and Flink?
Hadoop typically costs 30-50% less per TB for storage due to commodity hardware efficiency and 3x replication, making it ideal for cold data storage. Flink requires more memory but fewer nodes, resulting in 20-40% lower operational costs for streaming workloads. The choice depends on whether you prioritize storage cost (Hadoop) or compute efficiency (Flink).
Yes, many organizations use Hadoop for batch ETL and historical analytics while using Flink for real-time streams from the same data sources. Flink can read from HDFS, and both can integrate with Kafka, making a hybrid architecture viable for complex data pipelines requiring both batch and real-time processing.
How to Choose Between Apache Hadoop and Apache Flink
Processing Model: Apache Hadoop has Batch processing only, while Apache Flink has Stream and batch processing.
Latency: Apache Hadoop has Minutes to hours, while Apache Flink has Milliseconds to seconds.
State Management: Apache Hadoop has Limited, external storage required, while Apache Flink has Native, in-memory state backend.
Memory Efficiency: Apache Hadoop has Disk-based, slower I/O, while Apache Flink has In-memory processing, faster.
Ecosystem Maturity: Apache Hadoop has 15+ years, extensive integrations, while Apache Flink has 10+ years, growing integrations.
Community Size: Apache Hadoop has ~30,000+ commits on GitHub, while Apache Flink has ~25,000+ commits on GitHub.
Use Case Fit: Apache Hadoop has Historical data analysis, ETL, while Apache Flink has Real-time analytics, CEP, fraud detection.