What is the difference between Apache Spark and Hadoop MapReduce?

What is the difference between Apache Spark and Hadoop MapReduce?

Apache Spark and Hadoop MapReduce are both powerful big data processing frameworks, but they have some key differences:

• Processing Model: Hadoop MapReduce processes data in batch mode, while Apache Spark supports both batch and real-time processing.
• Speed: Apache Spark is generally faster than Hadoop MapReduce due to its in-memory processing capabilities.
• Ease of Use: Apache Spark provides a more user-friendly API compared to Hadoop MapReduce, making it easier for developers to write complex data processing tasks.
• Data Processing: Apache Spark provides a rich set of libraries for various data processing tasks like SQL, streaming, machine learning, and graph processing, while Hadoop MapReduce focuses mainly on batch processing.
• Resource Management: Apache Spark uses its own cluster manager and can run on top of Hadoop YARN, while Hadoop MapReduce relies on Hadoop's MapReduce JobTracker for resource management.

Overall, Apache Spark is favored for its speed, versatility, and ease of use, while Hadoop MapReduce is still commonly used for traditional batch processing tasks.