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What is the difference between Apache Spark and Apache Hadoop MapReduce?

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Some of the main differences between Apache Spark and Hadoop MapReduce are follows:

1. Speed: Apache Spark is 10X to 100X faster than Hadoop due to its usage of in memory processing.


2. Memory: Apache Spark stores data in memory, whereas Hadoop MapReduce stores data in hard disk.

3. RDD: Spark uses Resilient Distributed Dataset (RDD) that guarantee fault tolerance. Where Apache Hadoop uses replication of data in multiple copies to achieve fault tolerance.

4. Streaming: Apache Spark supports Streaming with very less administration. This


makes it much easier to use than Hadoop for real-time stream processing.

5. API: Spark provides a versatile API that can be used with multiple data sources as well as languages. It is more extensible than the API provided by Apache Hadoop.


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