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
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.
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.
Hadoop vs Apache Flink — Comparison Data
Structured comparison data for Hadoop vs Apache Flink with 164 attributes.
Total Cost of Ownership (5 years, 100TB)
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
Required Team Skills (FTE equivalents for operations)
Query Latency (median, standard ETL workload)
Built-in Collaboration Tools (notebooks, dashboards, repos)
Infrastructure Cost (Annual, 50TB dataset)
Infrastructure Cost (Annual, 500TB dataset)
Annual Cost (100TB/year, 5-node baseline)
Collaborative Notebooks with Version Control
Query Performance (10TB TPC-DS benchmark)
Time to First Correct Result (learning curve)
Time-to-Production (Simple Real-time Dashboard)
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.