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by The Captain

May 23, 2023

AWS EMR Tutorial: Using Amazon EMR to Process Big Data Workloads

Amazon Elastic MapReduce (EMR) is a popular big data processing service provided by AWS. EMR makes it easy to process large data sets by providing pre-configured clusters for running Apache Hadoop, Spark, and other big data frameworks. EMR clusters can scale automatically, making it easy to process even extremely large data sets.

Creating an EMR Cluster

The first step to using EMR is to create a cluster. To create an EMR cluster, you'll need to specify various configuration options, such as the size and number of nodes in the cluster, the version of Hadoop or Spark to use, and the location of your input and output data. You can create an EMR cluster using the AWS Management Console, the AWS CLI, or the AWS SDKs.

Once you've created a cluster, you can submit jobs to it using Hadoop or Spark. EMR provides a web-based interface for monitoring cluster activity, including CPU and memory usage, disk I/O, and network activity. You can also configure alarms to alert you when certain conditions are met, such as when a cluster is running out of disk space.

Using EMR with Hadoop

If you're using EMR with Hadoop, you'll submit jobs using the Hadoop command line interface. Hadoop provides a number of built-in map and reduce functions that you can use to process your data. You can also use custom Java code to create your own map and reduce functions.

Hadoop stores data on Hadoop Distributed File System (HDFS), a distributed file system that provides fault-tolerance and scalability. HDFS is designed to handle large data sets, and can store petabytes of data across thousands of nodes.

Using EMR with Spark

If you're using EMR with Spark, you'll submit jobs using the Spark command line interface. Spark is a popular big data processing framework that provides a number of libraries for processing data in-memory. Spark is often used for processing large data sets that are too large to fit in memory.

Unlike Hadoop, Spark doesn't require HDFS to store data. Instead, Spark provides its own data storage system, called Resilient Distributed Datasets (RDDs). RDDs allow you to store data in-memory, making it available for processing much more quickly than if you were using disk-based storage.


Amazon EMR is a powerful big data processing service that makes it easy to process large data sets using Hadoop, Spark, and other big data frameworks. With EMR, you can create scalable clusters that can handle even petabytes of data, and you can monitor cluster activity using a web-based interface. Whether you're processing log files, analyzing customer data, or performing machine learning tasks, EMR can help you get the job done quickly and efficiently.