What is the difference between Hadoop MapReduce and Spark for data processing in Big Data?

Difference between Hadoop MapReduce and Spark for Big Data Processing

Hadoop MapReduce and Spark are two popular frameworks used for processing data in the Big Data domain. Here are the key differences between Hadoop MapReduce and Spark:

1. Processing Speed: Hadoop MapReduce is generally slower than Spark because of its reliance on disk storage for intermediate results, while Spark performs in-memory processing which makes it faster.
2. Complexity: Hadoop MapReduce requires more boilerplate code for implementing data processing tasks, while Spark provides a more user-friendly API that simplifies development.
3. Resilience: Spark has built-in fault tolerance due to its ability to store intermediate results in-memory, making it more resilient to failures compared to Hadoop MapReduce.
4. Data Processing Models: Hadoop MapReduce follows a batch processing model, while Spark supports both batch and real-time processing through its streaming capabilities.
5. Ecosystem: Spark has a rich ecosystem with libraries for machine learning, graph processing, and streaming analytics, while Hadoop MapReduce is more focused on batch processing.

Overall, the choice between Hadoop MapReduce and Spark for data processing in Big Data depends on factors such as speed, complexity, resilience, data processing requirements, and the desired ecosystem for additional functionalities.