The key differences between Apache Spark and Hadoop are specified below:
- Apache Spark is designed to efficiently handle real-time data, whereas Hadoop is designed to efficiently handle batch processing.
- Apache Spark is a low latency computing and can process data interactively, whereas Hadoop is a high latency computing framework, which does not have an interactive mode.
Let's compare Hadoop and Spark-based on the following aspects:
| Feature Criteria | Apache Spark | Hadoop |
|---|
| Speed: | Apache Spark is 100 times faster than Hadoop. | It is also very fast but not as much as Apache Spark. |
| Processing: | It is used for Real-time & Batch processing. | This is used for Batch processing only. |
| Learning Difficulty: | It is easy to learn because of high-level modules. | It is tough to learn. |
| Interactivity: | It has interactive modes. | It doesn't have interactive modes except for Pig & Hive. |
| Recovery: | Allows recovery of partitions | Fault-tolerant |
