software
Hadoop vs Flink 2026: Batch vs Stream Processing
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.
AH
Apache Hadoop
Distributed batch processing framework for large-scale data storage and analysis using MapReduce paradigm.
Organizations processing large historical datasets, running batch ETL pipelines, and needing cost-effective long-term data storage at scale.
Score71%
VS
Apache Flink
Modern distributed stream processing framework with event-time semantics and exactly-once guarantees.
Real-time streaming applications, complex event processing, continuous data pipelines, and organizations prioritizing low latency over historical batch processing scale.
Score71%
Quick Answer
AI SummaryHadoop 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.
Our Verdict
AI-assistedChoose 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.
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A
Apache Hadoop
5.9/10
Apache Flink
9.1/10
A
Choose Apache Hadoop if
Organizations processing large historical datasets, running batch ETL pipelines, and needing cost-effective long-term data storage at scale.
Choose Apache Flink if
Best pickReal-time streaming applications, complex event processing, continuous data pipelines, and organizations prioritizing low latency over historical batch processing scale.
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Key Differences at a Glance
- Processing Model:✓ Apache Flink wins(Stream and batch processing vs Batch processing only)
- Latency:✓ Apache Flink wins(Milliseconds to seconds vs Minutes to hours)
- State Management:✓ Apache Flink wins(Native, in-memory state backend vs Limited, external storage required)
Key Facts & Figures
130 numeric metrics compared
| Metric | Apache Hadoop | Apache Flink | 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(seconds) | 300-1800 seconds (5-30 minutes) | 100-500 milliseconds | |
| Throughput (Records/Second)(million records/sec) | 100K-500K | 1M-10M | |
| Memory Usage per Node(GB) | 8-32 GB | 4-16 GB | |
| Supported Languages(languages) | 2 (Java, Scala) | 4 (Java, Scala, Python, SQL) | |
| GitHub Stars (2025)(stars) | 12.4K | 23.8K | |
| Optimal Dataset Size(GB minimum) | 100+ GB batches | Continuous streams (any size) | — |
| 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) | 5 languages (Java, Python, Scala, C++, Ruby) | 4 languages (Java, Scala, Python, SQL) | |
| 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 | — | — |
| 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 | — | — |
| 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 | — | — |
| 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 | — | — |
| Annual Cost (100TB, 24/7 usage)(USD) | $120,000 | — | — |
| Required DevOps Team Size(FTE) | 3-5 engineers | — | — |
| Community Size (GitHub Stars)(stars) | 14,200 stars | — | — |
| Throughput Capacity(events per second) | 1M-10M per node | 1M-100M per node | |
| Memory Usage per Task(MB) | 500-2000 MB | 100-800 MB | |
| Project Age(years) | 15 years (since 2006) | 10 years (since 2014) | |
| GitHub Stars(stars) | 14,500+ stars | 23,500+ stars | |
| State Backend Options(null) | External solutions required (HBase, etc.) | RocksDB (100GB+), HashMap, External KV stores | — |
| Minimum Cluster Size(nodes) | 3 nodes recommended | 1-2 nodes viable | |
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | 6-10 | |
| Available Built-in Connectors(count) | 50+ | 50+ | |
| Typical Throughput (single node)(events/sec) | 250,000 | 250,000 | |
| End-to-End Latency (p99)(milliseconds) | 5-50ms | 5-50ms | |
| State Size Capacity(GB) | 500+ | 500+ | |
| Processing Latency (end-to-end)(milliseconds) | 50-100ms | 50-100ms | |
| Setup Complexity (1-10)(difficulty) | 7/10 | 7/10 | |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | 8-12 weeks (with Kubernetes ops experience) | |
| Minimum Memory Requirement(GB) | 1024 MB | 1024 MB | |
| Production Deployments Reported(count) | 10,000+ | 10,000+ | |
| Programming Languages Supported(languages) | 4 (Java, Python, Scala, SQL) | 4 (Java, Python, Scala, SQL) | |
| First Release Year(year) | 2011 | 2011 | |
| Latency (p99 for simple aggregations)(milliseconds) | 100-500 ms (tuning dependent) | 100-500 ms (tuning dependent) | |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB) | Terabyte-scale (tested to 10+ TB) | |
| GitHub Stars (as of 2026)(stars) | 29,000+ | 29,000+ | |
| GitHub Stars (2026)(stars) | 23,800+ | 23,800+ | |
| Job Market Demand(active job postings) | ~1,850 | ~1,850 | |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | 1.5-2.5 GB | |
| Top-Level Apache Status(year achieved) | 2015 | 2015 | |
| Average Query Execution (1GB dataset)(seconds) | 2-3 seconds (streaming) / 4-6 (batch) | 2-3 seconds (streaming) / 4-6 (batch) | |
| Maximum Throughput per Node(events/second) | 100,000-1,000,000 events/sec | 100,000-1,000,000 events/sec | |
| Minimum Java Version Required(version) | Java 11+ | Java 11+ | |
| Native Connectors Available(count) | ~30 native connectors | ~30 native connectors | |
| Memory Overhead (per task)(MB) | ~200-400MB (optimized) | ~200-400MB (optimized) | |
| Throughput (events/sec per node)(events/sec) | ~1-2M events/sec | ~1-2M events/sec | |
| Initial Release Year(year) | 2014 | 2014 | |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | 2-4 GB | |
| Time to Build First Pipeline(hours) | 72 | 72 | |
| Active Contributors (6-month window)(developers) | 180+ | 180+ | |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | 0 (Open source) | |
| Community GitHub Stars(stars) | 10,400 | 10,400 | |
| Years in Production(years) | 12 (since 2014) | 12 (since 2014) | |
| Built-in Connectors(count) | 15+ | 15+ | |
| Max Throughput (Typical Setup)(events/sec) | Millions (1M+) | Millions (1M+) | |
| Event Latency (Processing End-to-End)(milliseconds) | 1-10ms (true event streaming) | 1-10ms (true event streaming) | |
| Memory Per Node(GB per 1M events/sec) | 6-8GB (efficient state management) | 6-8GB (efficient state management) | |
| Available Libraries & Integrations(count) | 2,000+ (Flink SQL, state backends, CEP library) | 2,000+ (Flink SQL, state backends, CEP library) | |
| Mean Time to Deploy Production Job(weeks) | 6-10 weeks (steeper learning curve, less documentation) | 6-10 weeks (steeper learning curve, less documentation) | |
| Stateful Window Operations Complexity(lines of code for session windows) | 15-30 lines (native session window API) | 15-30 lines (native session window API) | |
| Minimum Achievable Latency (P99)(milliseconds) | 100-500ms | 100-500ms | |
| GitHub Stars (Popularity Indicator)(stars) | 2,500 | 2,500 | |
| Market Adoption Rate(percentage of streaming workloads) | 15-20% | 15-20% | |
| Memory Overhead per Task(megabytes (baseline)) | 256-512MB | 256-512MB | |
| ANSI SQL Compliance(percentage) | 95% | 95% | |
| State Management Capabilities(feature count) | 5 types (keyed, operator, broadcast, queryable, custom) | 5 types (keyed, operator, broadcast, queryable, custom) | |
| Production Deployments (2026)(thousands of deployments) | 8,000-12,000 | 8,000-12,000 | |
| Year-over-Year Growth Rate(percentage) | 25% | 25% | |
| Minimum Processing Latency(milliseconds) | 1-10ms (streaming native) | 1-10ms (streaming native) | |
| Available Integrations(count) | 200+ | 200+ | |
| Typical Cluster Setup Complexity(complexity score (1-10)) | 7-9 (complex) | 7-9 (complex) | |
| Memory Per Task (Typical)(MB) | 2048-8192 | 2048-8192 | |
| Enterprise Adoption (2024)(% of tech companies) | 32% | 32% | |
| Time to Deploy Pipeline(hours) | 20-40 hours (learning + development) | 20-40 hours (learning + development) | |
| Minimum End-to-End Latency(milliseconds) | 500 ms | 500 ms | |
| Maximum Throughput(events per second) | Millions (100M+ with tuning) | Millions (100M+ with tuning) | |
| Minimum Memory Footprint(GB) | 2 GB (standalone single node) | 2 GB (standalone single node) | |
| Learning Curve (1-10 scale)(difficulty) | 8 | 8 | |
| Open Source Contributors(contributors) | 1,000+ | 1,000+ | |
| Production Deployments | 15,000+ | 15,000+ | |
| Time-to-Production (Simple Real-time Dashboard)(weeks) | 3-4 weeks | 3-4 weeks | |
| Minimum Latency (P99)(milliseconds) | 100-500ms | 100-500ms | |
| State Backend Memory Efficiency(GB per 1M records) | 2-3 GB | 2-3 GB | |
| SQL Standard Compliance(% compatibility) | 70% (subset with UDF limitations) | 70% (subset with UDF limitations) | |
| Production Deployments (2024)(organizations) | 10,000+ | 10,000+ | |
| Community Contributors (GitHub)(monthly active) | 120-150 | 120-150 | |
| Supported Source/Sink Connectors(count) | 80+ | 80+ | |
| Processing Latency (p99)(milliseconds) | 50-100ms | 50-100ms | |
| Throughput Per Node(events/second) | 10,000,000 | 10,000,000 | |
| Minimum Memory Per Worker(GB) | 2-4 | 2-4 | |
| GitHub Stars (Last 12 Months)(stars) | 2000+ | 2000+ |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
AH
2Apache HadoopApache Flink leads1 tie
Apache Hadoop
Attribute
Apache Flink
- Batch processing onlyProcessing ModelStream and batch processing(winner)
- Minutes to hoursLatencyMilliseconds to seconds(winner)
- Limited, external storage requiredState ManagementNative, in-memory state backend(winner)
- Disk-based, slower I/OMemory EfficiencyIn-memory processing, faster(winner)
- 15+ years, extensive integrations(winner)Ecosystem Maturity10+ years, growing integrations
- ~30,000+ commits on GitHub(winner)Community Size~25,000+ commits on GitHub
- Historical data analysis, ETLUse Case FitReal-time analytics, CEP, fraud detection
- Processing Model
Apache Hadoop
Batch processing only
Apache Flink
Stream and batch processing(winner)
- Latency
Apache Hadoop
Minutes to hours
Apache Flink
Milliseconds to seconds(winner)
- State Management
Apache Hadoop
Limited, external storage required
Apache Flink
Native, in-memory state backend(winner)
- Memory Efficiency
Apache Hadoop
Disk-based, slower I/O
Apache Flink
In-memory processing, faster(winner)
- Ecosystem Maturity
Apache Hadoop
15+ years, extensive integrations(winner)
Apache Flink
10+ years, growing integrations
- Community Size
Apache Hadoop
~30,000+ commits on GitHub(winner)
Apache Flink
~25,000+ commits on GitHub
- Use Case Fit
Apache Hadoop
Historical data analysis, ETL
Apache Flink
Real-time analytics, CEP, fraud detection
Full Comparison
| Attribute | Apache Hadoop | Apache Flink |
|---|---|---|
| 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 5 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 — Price (Self-Hosted)(USD/month) 0 (Open source) — | ||
| Setup Time(days) | 28-84 days | — |
| Initial Setup Time(hours) | 3 weeks | — |
| Initial Deployment Time(weeks) | 4-8 weeks | — |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | — |
| 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 5 more attributesMinimum Team Size(people) 4-8 (DevOps, engineers, admins) — Required DevOps Team Size(FTE) 3-5 engineers — Minimum Operational Complexity(components to manage) 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) — Deployment Complexity(complexity score (1-10)) Requires cluster with YARN/Kubernetes, moderate DevOps — Typical Cluster Setup Complexity(complexity score (1-10)) 7-9 (complex) — | ||
| Data Access Latency(milliseconds) | 20-50 ms | — |
| Processing Latency(seconds) | 300-1800 seconds (5-30 minutes) | 100-500 milliseconds(winner) |
| Throughput (Records/Second)(million records/sec) | 100K-500K | 1M-10M(winner) |
| Processing Speed (Same 1TB dataset)(seconds) | 300-600 seconds (disk-based) | — |
| Processing Speed vs MapReduce Baseline(times faster) | 1x (baseline) | — |
Show 28 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 — Throughput Capacity(events per second) 1M-10M per node 1M-100M per node Typical Throughput (single node)(events/sec) 250,000 — End-to-End Latency (p99)(milliseconds) 5-50ms — Processing Latency (end-to-end)(milliseconds) 50-100ms — Latency (p99 for simple aggregations)(milliseconds) 100-500 ms (tuning dependent) — Average Query Execution (1GB dataset)(seconds) 2-3 seconds (streaming) / 4-6 (batch) — Maximum Throughput per Node(events/second) 100,000-1,000,000 events/sec — Memory Overhead (per task)(MB) ~200-400MB (optimized) — Throughput (events/sec per node)(events/sec) ~1-2M events/sec — Max Throughput (Typical Setup)(events/sec) Millions (1M+) — Event Latency (Processing End-to-End)(milliseconds) 1-10ms (true event streaming) — Minimum Achievable Latency (P99)(milliseconds) 100-500ms — Minimum Processing Latency(milliseconds) 1-10ms (streaming native) — Minimum End-to-End Latency(milliseconds) 500 ms — Maximum Throughput(events per second) Millions (100M+ with tuning) — Minimum Latency (P99)(milliseconds) 100-500ms — State Backend Memory Efficiency(GB per 1M records) 2-3 GB — Processing Latency (p99)(milliseconds) 50-100ms — Throughput Per Node(events/second) 10,000,000 — | ||
| 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 | — |
| Minimum Cluster Size(nodes) | 3 nodes recommended | 1-2 nodes viable(winner) |
Show 2 more attributesState Size Capacity(GB) 500+ — Maximum Managed State Size(TB) Terabyte-scale (tested to 10+ TB) — | ||
| Availability SLA(percent uptime) | 95-99% (cluster-dependent) | — |
| Fault Tolerance Mechanism | Task re-execution + HDFS replication | Distributed snapshots + checkpointing |
| Fault Tolerance Method(mechanism) | Replication (3x copies) | — |
| Delivery Semantics | Exactly-once (native) | — |
| Processing Semantics | Exactly-once | — |
| Memory Usage per Node(GB) | 8-32 GB | 4-16 GB(winner) |
| Memory Usage per Task(MB) | 500-2000 MB | 100-800 MB(winner) |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | — |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | — |
| Memory Per Node(GB per 1M events/sec) | 6-8GB (efficient state management) | — |
Show 1 more attributeMemory Overhead per Task(megabytes (baseline)) 256-512MB — | ||
| Supported Languages(languages) | 2 (Java, Scala) | 4 (Java, Scala, Python, SQL)(winner) |
| GitHub Stars (2025)(stars) | 12.4K | 23.8K(winner) |
| Optimal Dataset Size(GB minimum) | 100+ GB batches | Continuous streams (any size) |
| Initial Setup Time to Production(weeks) | 8-12 weeks | — |
| On-Premises Deployment Option(availability) | Yes (full control) | — |
| Time-to-Production (Simple Real-time Dashboard)(weeks) | 3-4 weeks | — |
| Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) | $2,500-5,000 (infrastructure only) | — |
| 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 | — |
| Data Recovery (Time Travel)(days) | Manual snapshots only | — |
| State Backend Options(null) | External solutions required (HBase, etc.) | RocksDB (100GB+), HashMap, External KV stores |
Show 8 more attributesWatermark Support Yes (core feature) — Time Window Support Event-time, processing-time, session windows, custom — Programming Languages Supported(languages) 4 (Java, Python, Scala, SQL) — Supported Event Time Semantics Full with watermarks, out-of-order handling, allowedLateness — Batch+Stream Unified Code Separate APIs (DataStream vs Batch) — Built-in State Backends Memory, RocksDB, External (3 options) — Available Integrations(count) 200+ — Event-Time Support(null) Full support with watermarks and allowed lateness — | ||
| Setup Time to First Query(minutes) | 30-90 days (cluster + network + security) | — |
| Time to Build First Pipeline(hours) | 72 | — |
| Maximum Unstructured Data Support(% of typical use cases) | 90% (native HDFS support for any file type) | — |
| SQL Query Support | Hive SQL (SQL92, limited optimizations) | — |
| State Backend Storage Limit(scalability) | Terabytes of distributed state native | — |
| Data Residency Control(options) | Complete (on-premises or self-managed cloud) | — |
| Memory Requirement (Per Node)(GB) | 4-8 GB | — |
| First Release(year) | 2011 | — |
| Initial Release Year(year) | 2014 | — |
| Supported Programming Languages(languages) | 5 languages (Java, Python, Scala, C++, Ruby)(winner) | 4 languages (Java, Scala, Python, SQL) |
| Supported Source/Sink Connectors(count) | 80+ | — |
| 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) | — |
| Compute & Storage Coupling | Tightly coupled (scale together) | — |
| Primary Implementation Language | Java | — |
| 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 | — |
| Community Size (GitHub Stars)(stars) | 14,200 stars | — |
| GitHub Stars(stars) | 14,500+ stars | 23,500+ stars(winner) |
| GitHub Stars (as of 2026)(stars) | 29,000+ | — |
| GitHub Stars (2026)(stars) | 23,800+ | — |
| Active Contributors (6-month window)(developers) | 180+ | — |
Show 6 more attributesCommunity GitHub Stars(stars) 10,400 — Developer Community Size(millions) 1,800,000 (StackOverflow, job postings 2024) — GitHub Stars (Popularity Indicator)(stars) 2,500 — Open Source Contributors(contributors) 1,000+ — Community Contributors (GitHub)(monthly active) 120-150 — GitHub Stars (Last 12 Months)(stars) 2000+ — | ||
| Project Age(years) | 15 years (since 2006)(winner) | 10 years (since 2014) |
| First Release Year(year) | 2011 | — |
| Years in Production(years) | 12 (since 2014) | — |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | — |
| Production Deployments Reported(count) | 10,000+ | — |
| Market Adoption Rate(percentage of streaming workloads) | 15-20% | — |
| Production Deployments (2026)(thousands of deployments) | 8,000-12,000 | — |
| Production Deployments (2024)(organizations) | 10,000+ | — |
| Available Built-in Connectors(count) | 50+ | — |
| Native Connectors Available(count) | ~30 native connectors | — |
| Built-in Connectors(count) | 15+ | — |
| Available Libraries & Integrations(count) | 2,000+ (Flink SQL, state backends, CEP library) | — |
| Setup Complexity (1-10)(difficulty) | 7/10 | — |
| Minimum Memory Requirement(GB) | 1024 MB | — |
| Minimum Memory Footprint(GB) | 2 GB (standalone single node) | — |
| Minimum Memory Per Worker(GB) | 2-4 | — |
| Job Market Demand(active job postings) | ~1,850 | — |
| Machine Learning Capabilities(availability) | Limited (requires external libraries) | — |
| Top-Level Apache Status(year achieved) | 2015 | — |
| Python Support Level(support quality) | PyFlink added in v1.11 (2020); improved in v1.14+ | — |
| State Consistency Guarantee(semantic level) | Exactly-once (configurable per checkpoint) | — |
| Integrated Web UI(rating) | Basic REST API only (external UI required) | — |
| Minimum Java Version Required(version) | Java 11+ | — |
| Enterprise Adoption Rate(%) | 18% (Alibaba, Netflix, Uber, Lyft use cases) | — |
| Mean Time to Deploy Production Job(weeks) | 6-10 weeks (steeper learning curve, less documentation) | — |
| Stateful Window Operations Complexity(lines of code for session windows) | 15-30 lines (native session window API) | — |
| State Management Capabilities(feature count) | 5 types (keyed, operator, broadcast, queryable, custom) | — |
| ANSI SQL Compliance(percentage) | 95% | — |
| Year-over-Year Growth Rate(percentage) | 25% | — |
| Memory Per Task (Typical)(MB) | 2048-8192 | — |
| Enterprise Adoption (2024)(% of tech companies) | 32% | — |
| Time to Deploy Pipeline(hours) | 20-40 hours (learning + development) | — |
| Learning Curve (1-10 scale)(difficulty) | 8 | — |
| Production Deployments | 15,000+ | — |
| SQL Standard Compliance(% compatibility) | 70% (subset with UDF limitations) | — |
| Exactly-Once Semantics(null) | Native with distributed snapshots | — |
| Configuration Complexity(config parameters) | 60+ core parameters | — |
AApache Hadoop
Apache FlinkTotal 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 5 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
—
Price (Self-Hosted)(USD/month)
0 (Open source)
—
Setup Time(days)
28-84 days
—
Initial Setup Time(hours)
3 weeks
—
Initial Deployment Time(weeks)
4-8 weeks
—
Time to First Production Deployment(days)
8-12 weeks (with Kubernetes ops experience)
—
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 5 more attributes
Minimum Team Size(people)
4-8 (DevOps, engineers, admins)
—
Required DevOps Team Size(FTE)
3-5 engineers
—
Minimum Operational Complexity(components to manage)
5-7 (JobManager, TaskManagers, StateBackend, Checkpoints)
—
Deployment Complexity(complexity score (1-10))
Requires cluster with YARN/Kubernetes, moderate DevOps
—
Typical Cluster Setup Complexity(complexity score (1-10))
7-9 (complex)
—
Data Access Latency(milliseconds)
20-50 ms
—
Processing Latency(seconds)
300-1800 seconds (5-30 minutes)
100-500 milliseconds
Throughput (Records/Second)(million records/sec)
100K-500K
1M-10M
Processing Speed (Same 1TB dataset)(seconds)
300-600 seconds (disk-based)
—
Processing Speed vs MapReduce Baseline(times faster)
1x (baseline)
—
Show 28 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
—
Throughput Capacity(events per second)
1M-10M per node
1M-100M per node
Typical Throughput (single node)(events/sec)
250,000
—
End-to-End Latency (p99)(milliseconds)
5-50ms
—
Processing Latency (end-to-end)(milliseconds)
50-100ms
—
Latency (p99 for simple aggregations)(milliseconds)
100-500 ms (tuning dependent)
—
Average Query Execution (1GB dataset)(seconds)
2-3 seconds (streaming) / 4-6 (batch)
—
Maximum Throughput per Node(events/second)
100,000-1,000,000 events/sec
—
Memory Overhead (per task)(MB)
~200-400MB (optimized)
—
Throughput (events/sec per node)(events/sec)
~1-2M events/sec
—
Max Throughput (Typical Setup)(events/sec)
Millions (1M+)
—
Event Latency (Processing End-to-End)(milliseconds)
1-10ms (true event streaming)
—
Minimum Achievable Latency (P99)(milliseconds)
100-500ms
—
Minimum Processing Latency(milliseconds)
1-10ms (streaming native)
—
Minimum End-to-End Latency(milliseconds)
500 ms
—
Maximum Throughput(events per second)
Millions (100M+ with tuning)
—
Minimum Latency (P99)(milliseconds)
100-500ms
—
State Backend Memory Efficiency(GB per 1M records)
2-3 GB
—
Processing Latency (p99)(milliseconds)
50-100ms
—
Throughput Per Node(events/second)
10,000,000
—
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
—
Minimum Cluster Size(nodes)
3 nodes recommended
1-2 nodes viable
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State Size Capacity(GB)
500+
—
Maximum Managed State Size(TB)
Terabyte-scale (tested to 10+ TB)
—
Availability SLA(percent uptime)
95-99% (cluster-dependent)
—
Fault Tolerance Mechanism
Task re-execution + HDFS replication
Distributed snapshots + checkpointing
Fault Tolerance Method(mechanism)
Replication (3x copies)
—
Delivery Semantics
Exactly-once (native)
—
Processing Semantics
Exactly-once
—
Memory Usage per Node(GB)
8-32 GB
4-16 GB
Memory Usage per Task(MB)
500-2000 MB
100-800 MB
Baseline JVM Memory Overhead(GB)
1.5-2.5 GB
—
Memory Overhead (idle cluster)(GB)
2-4 GB
—
Memory Per Node(GB per 1M events/sec)
6-8GB (efficient state management)
—
Show 1 more attribute
Memory Overhead per Task(megabytes (baseline))
256-512MB
—
Supported Languages(languages)
2 (Java, Scala)
4 (Java, Scala, Python, SQL)
GitHub Stars (2025)(stars)
12.4K
23.8K
Optimal Dataset Size(GB minimum)
100+ GB batches
Continuous streams (any size)
Initial Setup Time to Production(weeks)
8-12 weeks
—
On-Premises Deployment Option(availability)
Yes (full control)
—
Time-to-Production (Simple Real-time Dashboard)(weeks)
3-4 weeks
—
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD)
$2,500-5,000 (infrastructure only)
—
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
—
Data Recovery (Time Travel)(days)
Manual snapshots only
—
State Backend Options(null)
External solutions required (HBase, etc.)
RocksDB (100GB+), HashMap, External KV stores
Show 8 more attributes
Watermark Support
Yes (core feature)
—
Time Window Support
Event-time, processing-time, session windows, custom
—
Programming Languages Supported(languages)
4 (Java, Python, Scala, SQL)
—
Supported Event Time Semantics
Full with watermarks, out-of-order handling, allowedLateness
—
Batch+Stream Unified Code
Separate APIs (DataStream vs Batch)
—
Built-in State Backends
Memory, RocksDB, External (3 options)
—
Available Integrations(count)
200+
—
Event-Time Support(null)
Full support with watermarks and allowed lateness
—
Setup Time to First Query(minutes)
30-90 days (cluster + network + security)
—
Time to Build First Pipeline(hours)
72
—
Maximum Unstructured Data Support(% of typical use cases)
90% (native HDFS support for any file type)
—
SQL Query Support
Hive SQL (SQL92, limited optimizations)
—
State Backend Storage Limit(scalability)
Terabytes of distributed state native
—
Data Residency Control(options)
Complete (on-premises or self-managed cloud)
—
Memory Requirement (Per Node)(GB)
4-8 GB
—
First Release(year)
2011
—
Initial Release Year(year)
2014
—
Supported Programming Languages(languages)
5 languages (Java, Python, Scala, C++, Ruby)
4 languages (Java, Scala, Python, SQL)
Supported Source/Sink Connectors(count)
80+
—
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)
—
Compute & Storage Coupling
Tightly coupled (scale together)
—
Primary Implementation Language
Java
—
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
—
Community Size (GitHub Stars)(stars)
14,200 stars
—
GitHub Stars(stars)
14,500+ stars
23,500+ stars
GitHub Stars (as of 2026)(stars)
29,000+
—
GitHub Stars (2026)(stars)
23,800+
—
Active Contributors (6-month window)(developers)
180+
—
Show 6 more attributes
Community GitHub Stars(stars)
10,400
—
Developer Community Size(millions)
1,800,000 (StackOverflow, job postings 2024)
—
GitHub Stars (Popularity Indicator)(stars)
2,500
—
Open Source Contributors(contributors)
1,000+
—
Community Contributors (GitHub)(monthly active)
120-150
—
GitHub Stars (Last 12 Months)(stars)
2000+
—
Project Age(years)
15 years (since 2006)
10 years (since 2014)
First Release Year(year)
2011
—
Years in Production(years)
12 (since 2014)
—
Time to First Correct Result (learning curve)(weeks (team of 2))
6-10
—
Production Deployments Reported(count)
10,000+
—
Market Adoption Rate(percentage of streaming workloads)
15-20%
—
Production Deployments (2026)(thousands of deployments)
8,000-12,000
—
Production Deployments (2024)(organizations)
10,000+
—
Available Built-in Connectors(count)
50+
—
Native Connectors Available(count)
~30 native connectors
—
Built-in Connectors(count)
15+
—
Available Libraries & Integrations(count)
2,000+ (Flink SQL, state backends, CEP library)
—
Setup Complexity (1-10)(difficulty)
7/10
—
Minimum Memory Requirement(GB)
1024 MB
—
Minimum Memory Footprint(GB)
2 GB (standalone single node)
—
Minimum Memory Per Worker(GB)
2-4
—
Job Market Demand(active job postings)
~1,850
—
Machine Learning Capabilities(availability)
Limited (requires external libraries)
—
Top-Level Apache Status(year achieved)
2015
—
Python Support Level(support quality)
PyFlink added in v1.11 (2020); improved in v1.14+
—
State Consistency Guarantee(semantic level)
Exactly-once (configurable per checkpoint)
—
Integrated Web UI(rating)
Basic REST API only (external UI required)
—
Minimum Java Version Required(version)
Java 11+
—
Enterprise Adoption Rate(%)
18% (Alibaba, Netflix, Uber, Lyft use cases)
—
Mean Time to Deploy Production Job(weeks)
6-10 weeks (steeper learning curve, less documentation)
—
Stateful Window Operations Complexity(lines of code for session windows)
15-30 lines (native session window API)
—
State Management Capabilities(feature count)
5 types (keyed, operator, broadcast, queryable, custom)
—
ANSI SQL Compliance(percentage)
95%
—
Year-over-Year Growth Rate(percentage)
25%
—
Memory Per Task (Typical)(MB)
2048-8192
—
Enterprise Adoption (2024)(% of tech companies)
32%
—
Time to Deploy Pipeline(hours)
20-40 hours (learning + development)
—
Learning Curve (1-10 scale)(difficulty)
8
—
Production Deployments
15,000+
—
SQL Standard Compliance(% compatibility)
70% (subset with UDF limitations)
—
Exactly-Once Semantics(null)
Native with distributed snapshots
—
Configuration Complexity(config parameters)
60+ core parameters
—
Pros & Cons
10 pros·4 cons across both
AH
AH
Apache Hadoop
+5-2
Pros
- Proven maturity with 15+ years of production deployments at scale (Facebook, Yahoo, Amazon)
- Excellent fault tolerance through data replication across 3+ nodes by default
- Massive ecosystem with 100+ integrated tools (Hive, Pig, Spark, HBase, Sqoop)
- Cost-effective for storing petabyte-scale datasets with commodity hardware
- HDFS provides 99.9% data availability guarantee
Cons
- High latency (typically 5-30 minutes) makes real-time processing impractical
- Steep learning curve for MapReduce programming paradigm; requires Java expertise
Apache Flink
+5-2
Pros
- Sub-second to millisecond latency enables real-time fraud detection and anomaly detection use cases
- Native support for complex stateful operations with managed in-memory state backends (RocksDB, memory)
- Unified framework for batch and streaming with same API, eliminating dual-pipeline complexity
- Advanced event time processing with watermarks for handling out-of-order and late-arriving data
- Lower memory footprint per throughput compared to Hadoop; processes 1M events/sec on smaller clusters
Cons
- Smaller ecosystem compared to Hadoop; fewer third-party integrations (improving but still lagging)
- Steeper operational complexity for state backend configuration and checkpointing mechanisms
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
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Wikipedia
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