Difference Between EMR and Glue

Pre-requisite:- AWS

Amazon Web Services (AWS), a subsidiary of Amazon.com, has invested billions of dollars in IT resources distributed across the globe. These resources are shared among all the AWS account holders across the globe. These accounts themselves are entirely isolated from each other. AWS provides on-demand IT resources to its account holders on a pay-as-you-go pricing model with no upfront cost.

Glue

For analytics, machine learning (ML), and application development, AWS Glue is a serverless data integration service that makes it simpler to find, prepare, move, and combine data from many sources. The first step in any analytics or machine learning project is to prepare your data to ensure high-quality outcomes. AWS Glue is a serverless data integration service that streamlines, accelerates, and reduces the cost of data preparation. In order to load data into your data lakes, you can easily construct, run, and monitor ETL pipelines and identify and connect to over 70 data sources. You can also manage your data in a centralized data catalog.

Users of AWS Glue have a variety of interface options from which to develop job workloads that leverage different data integration engines.

EMR – Elastic Map Reduce

Amazon EMR is the market-leading cloud big data solution for processing data at a petabyte scale, doing interactive analytics, and performing machine learning. Using the new Amazon EMR serverless option, data engineers and analysts can execute applications created with open-source big data frameworks like Apache Spark, Hive, or Presto quickly and affordably without having to calibrate, operate, optimize, secure, or manage clusters.

Difference between EMR and Glue Tool

Objective	AWS EMR	AWS GLUE
Definition	It is a cloud-based managed service that heavily relies on Amazon S3 to store data sets for processing and analysis results and uses Amazon EC2 to process large amounts of data across a cluster of virtual computers.	AWS Glue is a serverless data integration service that makes it simpler to find, prepare, move, and combine data from many sources for analytics, machine learning (ML), and application development
Flexibility and Scalability	The configuration and management of the cluster of Apache Hadoop and Map Reduce components are made simpler by the use of Amazon EMR, a fully managed cluster platform. It offers a straightforward method of scaling ongoing workloads in accordance with your processing needs. You can establish one or more instance groups for processing in addition to resizing your cluster as necessary.	Due to the fact that it operates in a fully managed, serverless environment, AWS Glue is also adaptable and simple to scale. In a scale-out Apache context, it creates highly scalable ETL jobs for distributed processing.
Use Cases	EMR environment offers both the processing capacity and the on-demand infrastructure needed to swiftly and affordably analyze massive amounts of data. EMR makes it easier to run big data frameworks on AWS for processing big data at scales, such as Apache Spark and Apache Hadoop. It frequently serves as a good alternative to on-premises Hadoop migrations.	It is a serverless ETL technology that aids in data discovery, organization, and crawling so that it is ready for analytics. For fresh workloads, it works best.
Price Comparison	It is less expensive because it already has the necessary configuration. You are charged on a per-second basis, which means you must pay at least one minute for every second you use.	As it is a serverless platform, AWS Glue is more expensive. For crawlers and ETL jobs, you are charged by the second, and the AWS Glue cost is based on data processing units.