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Table of contents
1.
Introduction
2.
What Is Amazon EMR
3.
How to use Amazon EMR
4.
Features of AWS EMR
5.
Frequently Asked Questions
5.1.
What does AWS do?
5.2.
What is Amazon EMR?
5.3.
What is EMR built on?
5.4.
What are the differences between AWS EC2 and EMR?
5.5.
What are the benefits of Amazon EMR?
6.
Conclusion
Last Updated: Mar 27, 2024
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Amazon EMR

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Introduction

Statista estimates that the total amount of data created, stored, duplicated, and consumed in 2020 will be about 64 trillion GB.

A significant percentage of this data is likely to be helpful to your company. It can give you new insights into your product, help you communicate with customers, and undertake risk assessments. You'll need the correct tools to extract, sort, process, and analyze information.

This is where Amazon's Elastic MapReduce (EMR) tool comes in. We'll go through what EMR is, how it works, and how it can help you in this article. You may then evaluate whether it's worth incorporating into your big data strategy.

What Is Amazon EMR

Amazon EMR is built on Apache Hadoop, a Java-based programming platform for large-scale data processing in a distributed environment. Developers can design programs that handle vast volumes of unstructured data across a distributed cluster of processors or standalone machines using MapReduce, a significant component of the Hadoop software framework. Google created it in 2004 to replace its initial indexing algorithms and heuristics for indexing webpages.

Amazon EMR uses Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service to process massive data across a Hadoop cluster of virtual computers (S3). The Elastic in EMR's name refers to its dynamic scaling capability, allowing managers to scale up or down resources based on current requirements.

We are done with the introduction. Let's move on to the discussion about Amazon EMR.

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How to use Amazon EMR

1. Develop your data processing application: Amazon EMR is built on Apache Hadoop, a Java-based programming platform for large-scale data processing in a distributed environment. Developers can design programs that handle vast volumes of unstructured data across a distributed cluster of processors or standalone machines using MapReduce, a significant component of the Hadoop software framework. Google created it in 2004 to replace its initial indexing algorithms and heuristics for indexing webpages.

2. Upload your application and data to Amazon S3: Amazon EMR uses Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service to process massive data across a Hadoop cluster of virtual computers (S3). The Elastic in EMR's name refers to its dynamic scaling capability, allowing managers to scale up or down resources based on the running cluster.

source: docs.aws.amazon.com
 

3. Configure and launch your cluster: Specify the number of Amazon EC2 instances to a provision in your cluster, the types of instances to use, the applications to install (Apache Spark, Apache Hive, etc.), and the location of your application and data using the AWS Management Console, AWS CLI, SDKs, or APIs. Bootstrap Actions can be used to install additional software or modify default settings.

4. Monitor the cluster: The Management Console, Command Line Interface, SDKs, and APIs can all be used to keep track of the cluster's health and progress. EMR supports standard monitoring tools like Ganglia and connects with Amazon CloudWatch for monitoring and alarms. To handle more or less data, you can add or remove capacity from the cluster at any moment. You can utilize the console's simple debugging GUI for troubleshooting.

5. Retrieve the output: On the cluster, get the output from Amazon S3 or HDFS. Use Amazon QuickSight, Tableau, and MicroStrategy technologies to visualize the data. Amazon EMR will immediately shut down the cluster when the processing is finished. You can also leave the cluster operating and assign it additional tasks.

Features of AWS EMR

Let's look at some of the features of AWS EMR now:

1. Adaptability

AWS EMR simplifies the creation and management of massive data platforms and apps. EMR features include easy provisioning, controlled scaling, cluster reconfiguration, and EMR Studio for coherent development.

2. Elasticity

AWS EMR allows you to quickly and efficiently supply as much capacity as you need and add various capacities manually or automatically. This is especially useful if your processing needs are unpredictable or vary frequently.

3. Flexibility

AWS EMR is highly flexible. Amazon S3, Amazon DynamoDB, and Hadoop Distributed File System (HDFS) are among the data stores available with AWS EMR.

4. Tools for Big Data

AWS EMR supports Hadoop technologies such as Apache Spark, Apache Hive, Presto, and Apache HBase. EMR allows data scientists to leverage bootstrap operations to implement deep learning and associated technologies, such as TensorFlow and Apache MXNet, and scenario tools and frameworks.

5. Data Access

AWS EMR application processes use the EC2 instance account by default when calling other Amazon Web Services. EMR offers three options for managing user access to Amazon S3 data in multi-tenant clusters.

We’ve done with the blog; let's move to faqs.

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Frequently Asked Questions

What does AWS do?

AWS is a broadly adopted cloud platform that offers several on-demand operations like compute power, database storage, content delivery, etc., to help corporates scale and grow.

What is Amazon EMR?

Amazon EMR (formerly known as Amazon Elastic MapReduce) is a managed cluster platform that makes it easier to run big data frameworks on AWS, such as Apache Hadoop and Apache Spark, to process and analyze large volumes of data.

What is EMR built on?

Amazon EMR is built on Apache Hadoop, a Java-based programming platform for large-scale data processing in a distributed computing environment.

What are the differences between AWS EC2 and EMR?

Amazon EC2 is a cloud-based service that gives users access to various compute instances or virtual machines. In contrast, Amazon EMR is a managed big data service offering Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto pre-configured compute clusters.

What are the benefits of Amazon EMR?

Amazon EMR provides Cost savings, AWS integration, Deployment, Scalability, and flexibility.

Conclusion

In this article, we have extensively discussed Amazon EMR. We start with a brief introduction to Amazon EMR, then discuss how to use it and its features.

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