Hadoop Vs Apache Storm Vs Apache beam
Hadoop, Apache Storm, Apache Beam, and Apache Spark are all open-source big data processing frameworks that are used to process large amounts of data. However, each of these frameworks has its own strengths and use cases.
Here’s a comparison of Hadoop, Apache Storm, Apache Beam, and Apache Spark in tabular format:
| Feature | Hadoop | Apache Storm | Apache Beam | Apache Spark |
|---|---|---|---|---|
| Type of Processing | Batch | Stream | Batch & Stream | Batch & Stream |
| Latency | High | Low | High | High |
| Data Processing Model | MapReduce | Stream | Unified Model | Micro-batch |
| Memory Management | Manual | Automatic | Automatic | Automatic |
| Fault Tolerance | Yes | Yes | Yes | Yes |
| State Management | HDFS | Zookeeper | Portable | In-memory |
| API | MapReduce | Trident | Portable | RDD, DataFrame, SQL |
| Machine Learning | Mahout | N/A | N/A | MLlib |
| Use Cases | Batch processing, Data warehousing, Data lakes | Streaming, Real-time Processing, Complex Event Processing | Complex data processing pipelines, Cross-engine, Cross-environment | Batch processing, SQL, Streaming, Machine Learning |
- Hadoop is a distributed storage and processing framework that is designed to handle big data workloads and it is well-suited for batch processing and offline analysis.
- Apache Storm is a distributed real-time computation system that is designed to handle high-throughput, low-latency data streams. It’s good for use cases such as real-time analytics, online machine learning, and continuous computation.
- Apache Beam is an open-source, unified programming model for both batch and streaming data processing, it is good for use cases that involve complex data processing pipelines and need to run on multiple engines and environments.
- Apache Spark is a fast and general-purpose cluster computing system that is designed for batch processing, SQL, streaming, and machine learning workloads. It’s good for iterative and interactive workloads and it provides a high-level API for data processing and a built-in cluster