Understanding Amazon EMR: A Guide to Clusters and Nodes in Big Data Processing

What is Amazon EMR

Amazon EMR, formerly known as Amazon Elastic MapReduce, serves as a managed cluster platform designed to streamline the execution of significant data frameworks like Apache Hadoop and Apache Spark on AWS. Its purpose is to facilitate the processing and analysis of extensive data sets. By leveraging these frameworks and associated open-source initiatives, users can handle data for analytical tasks and business intelligence workloads. Furthermore, Amazon EMR enables the efficient transformation and transfer of substantial data volumes to and from various AWS data repositories and databases, including Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.

Understanding clusters and nodes

Primary node

The primary node serves as the cluster manager and typically handles key components of distributed applications. For instance, it runs the YARN ResourceManager service for resource management in applications and operates the HDFS NameNode service. Additionally, it keeps tabs on job statuses within the cluster and monitors the well-being of instance groups.

To Monitor the cluster’s progress and engage directly with applications, connecting to the Primary node via SSH as the Hadoop user is an option. This connection provides access to directories and files, including direct retrieval of Hadoop log files. Furthermore…