Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data.

Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances. You can also run other popular distributed frameworks such as Spark and Presto in Amazon EMR, and interact with data in other AWS data stores such as Amazon S3 and Amazon DynamoDB.

Amazon EMR securely and reliably handles your big data use cases, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Introduction to Amazon EMR (3:06)


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If you have large data processing requirements, you may be eligible for lower pricing.

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Guest post on Using BlueTalon with Amazon EMR, by Pratik Verma, Founder and Chief Product Officer at BlueTalon.

Easy to Use

You can launch an Amazon EMR cluster in minutes. You don’t need to worry about node provisioning, cluster setup, Hadoop configuration, or cluster tuning. Amazon EMR takes care of these tasks so you can focus on analysis.

Low Cost

Amazon EMR pricing is simple and predictable: You pay an hourly rate for every instance hour you use. You can launch a 10-node Hadoop cluster for as little as $0.15 per hour. Because Amazon EMR has native support for Amazon EC2 Spot and Reserved Instances, you can also save 50-80% on the cost of the underlying instances.


With Amazon EMR, you can provision one, hundreds, or thousands of compute instances to process data at any scale. You can easily increase or decrease the number of instances and you only pay for what you use.


You can spend less time tuning and monitoring your cluster. Amazon EMR has tuned Hadoop for the cloud; it also monitors your cluster —retrying failed tasks and automatically replacing poorly performing instances.


Amazon EMR automatically configures Amazon EC2 firewall settings that control network access to instances, and you can launch clusters in an Amazon Virtual Private Cloud (VPC), a logically isolated network you define.  For objects stored in Amazon S3, you can use Amazon S3 server-side encryption or Amazon S3 client-side encryption with EMRFS, with AWS Key Management Service or customer-managed keys.


You have complete control over your cluster. You have root access to every instance, you can easily install additional applications, and you can customize every cluster. Amazon EMR also supports multiple Hadoop distributions and applications.

Amazon EMR can be used to analyze click stream data in order to segment users and understand user preferences. Advertisers can also analyze click streams and advertising impression logs to deliver more effective ads.

Learn how Razorfish uses EMR for click stream analysis »

Amazon EMR can be used to process vast amounts of genomic data and other large scientific data sets quickly and efficiently. Researchers can access genomic data hosted for free on AWS.

Read about the 1000 Genomes project and AWS »

Amazon EMR can be used to process logs generated by web and mobile applications. Amazon EMR helps customers turn petabytes of un-structured or semi-structured data into useful insights about their applications or users.

Learn how Yelp uses EMR to drive key website features »

To use Amazon EMR, you simply:

  1. Develop your data processing application. You can use Java, Hive (a SQL-like language), Pig (a data processing language), Cascading, Ruby, Perl, Python, R, PHP, C++, or Node.js. Amazon EMR provides code samples and tutorials to get you up and running quickly.
  2. Upload your application and data to Amazon S3. If you have a large amount of data to upload, you may want to consider using AWS Import/Export (to upload data using physical storage devices) or AWS Direct Connect (to establish a dedicated network connection from your data center to AWS). If you prefer, you can also write your data directly to a running cluster.
  3. Configure and launch your cluster. Using the AWS Management Console, the AWS CLI, SDKs, or APIs, specify the number of EC2 instances to provision in your cluster, the types of instances to use (standard, high memory, high CPU, high I/O, etc.), the applications to install (Hive, Pig, HBase, etc.), and the location of your application and data. You can use Bootstrap Actions to install additional software or change default settings.
  4. (Optional) Monitor the cluster. You can monitor the cluster’s health and progress using the Management Console, Command Line Interface, SDKs, or APIs. EMR integrates with Amazon CloudWatch for monitoring/alarming and supports popular monitoring tools like Ganglia. You can add/remove capacity to the cluster at any time to handle more or less data. For troubleshooting, you can use the console’s simple debugging GUI.
  5. Retrieve the output. Retrieve the output from Amazon S3 or HDFS on the cluster. Visualize the data with tools like Tableau and MicroStrategy. Amazon EMR will automatically terminate the cluster when processing is complete. Alternatively you can leave the cluster running and give it more work to do.

Are you ready to launch your first cluster? Click here to view the Getting Started Tutorial. In the tutorial you will create a cluster that will count the frequency of words in a sample text file. In just a few minutes your cluster will be up and running.