Apache Spark and Hadoop MapReduce are two of the most popular big data processing frameworks.
The main difference between Apache Spark and Hadoop MapReduce is the way they process data. Hadoop MapReduce processes data in a batch-oriented fashion, while Apache Spark processes data in a real-time, streaming fashion.
For example, if you wanted to analyze a large dataset with Hadoop MapReduce, you would have to first store the data in HDFS and then write a MapReduce program to process the data. The program would then be submitted to the Hadoop cluster and the results would be returned after the job is completed.
On the other hand, with Apache Spark, you can process the data in real-time as it is being streamed in. This means that you can get the results much faster and with less effort. Additionally, Spark is more versatile and can be used for a variety of tasks, such as machine learning, graph processing, and streaming analytics.