Which feature of Amazon EMR is used to process big data?

The feature of Amazon EMR primarily responsible for processing big data is Hadoop and Spark. Amazon EMR (Elastic MapReduce) provides a managed framework for running big data frameworks, primarily Hadoop, Apache Spark, and other tools.

Hadoop is a well-known framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. It handles storage and processing through its components, including the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.

Apache Spark, on the other hand, is a unified analytics engine designed for large-scale data processing. It offers in-memory data processing capabilities which can significantly speed up data processing tasks compared to MapReduce. Spark can run on top of Hadoop, leveraging its storage capabilities while providing a faster and more flexible processing engine.

This combination of Hadoop and Spark on Amazon EMR provides powerful tools that enable users to manage and analyze vast amounts of data efficiently, making it a crucial aspect of big data processing within the AWS ecosystem. The flexibility to choose between these two frameworks and the ease of scaling them according to workload demands are what ultimately make Amazon EMR a strong option for big data processing.