You might have heard the name of Mr. Paul Graham if you are a developer or hacker. If you haven’t heard, that’s also not an issue because he is not the one who created Amazon EMR, or we say Amazon Elastic MapReduce. However, once he said something that you might relate sooner or later:

In programming, as in many fields, the hard part isn’t solving problems, but deciding what problems to solve.

NASA/JPL’s Mars Curiosity Mission Case Study

August 6, 2012, million of miles away from Earth, a Mars rover named Curiosity landed on the red planet. A great achievement of engineering and technical expertise into this mission, isn’t it. One more aspect that was equally exciting about this mission was the information technology behind this mission and the use of AWS services by NASA’s Jet Propulsion Laboratory (JPL). Just before the landing, NASA was able to purvey stacks of AWS infrastructure to support 25 Gbps of throughput to provide NASA’s fans and scientists up-to-date information about their rover and landing. Afterward, NASA continues to use AWS for analysis of data and gives scientists access to data they gather from that mission.

Now, the question arouses how this is related to Amazon Elastic MapReduce? Well, earlier access to such type of services or resources were available only to governments or large multi-national organizations. Now, the power of analyzing volumes of data and support to high volumes of traffic in a moment is available to anyone who owns a laptop and a credit card. What used to take months— with the development of large data centers, computing hardware, and networking — now can be accomplished quickly and for short-term AWS projects.

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Modern Problems needs modern solutions

Today, businesses are trying to understand their customers’ behavior and identify trends as soon as possible to stay ahead of their competition. In finance and corporate security, companies are overwhelmed with terabytes and petabytes of information. IT departments with strict budget complications are asked to make sense of the ever-increasing volume of data and to help companies stay ahead of the game. Hadoop and MapReduce framework are the only tools to ignite the fire. Great power comes with greater complications; similarly, this has not eliminated the expense and time required to build and maintain a vast IT infrastructure to carry out this research in the traditional data center.

What is Amazon Elastic MapReduce (EMR)?

Amazon Elastic MapReduce is one of the many services that AWS offers. It enables users to launch and use resizable Hadoop clusters within Amazon’s infrastructure. Like Hadoop, Amazon EMR can be used to analyze vast data sets. It also simplifies the setups and management of the cluster of Hadoop and MapReduce components. EMR use Amazon’s prebuilt and customized EC2 instances, that can take full advantage of Amazon’s infrastructure and other services offered by AWS. Such EC2 instances are invoked when we initiate a new Job Flow to form an EMR cluster. A Job Flow is Amazon’s term for complete data processing that occurs through a series of computational steps in Amazon’s EMR. A Job Flow is defined by the MapReduce framework and its input and output parameters.

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Working

Amazon EMR does the computational analysis with the help of the MapReduce framework. The MapReduce framework breaks the input data into smaller fragments or shards, that distribute it to the nodes that compose the cluster.

It’s important to note that a Job Flow is carried out on a series of EC2 instances running the Hadoop components that are broken up into master, core, and task clusters. Now, these data fragments are processed individually by MapReduce application running on each of the core and task nodes in the cluster. 

Benefits

1. EMR starts the cluster in quick succession. You don’t need to bother about the node provision, its infrastructural setup, Hadoop configuration, or cluster tuning. EMR takes care of all the tasks so that you can focus on analysis.
2. Pricing of EMR is entirely predictable and straightforward: you only need to pay-per-instance rate for every second you use, with a one-minute minimum charge. For as little as $0.15 per hour, you can launch a 10-node EMR cluster with applications like Apache Spark and Apache Hive.
3. With Elastic MapReduce, you can provision hundreds or thousands of compute resources to process the data at any scale. While the number of instances can be increased and decreased manually or automatically using Auto Scaling, and you pay only for what you use.
4. EMR is tuned to the cloud and continuously monitors your cluster— retrieving failed tasks and automatically replacing poorly performing instances. It helps you to save much of your time.
5. EMR configures EC2 firewall settings that control network access to instances automatically and launches clusters in an Amazon Virtual Private Cloud (VPC), which is a logically isolated network you define.
6. Elastic MapReduce provides complete control over your clusters. You can have root access to each of your instances; You can easily install additional applications and use bootstrap actions to customize every cluster.