This is an era of unlimited information that which is collected, stored, processed and used across platforms. It is being generated every day in various forms through the increased usage of electronic transactions, social media, internet and more. While traditional databases have been declared inefficient in handling those huge chunks of information, it is believed mandatory to manage big data efficiently for the future use, in every possible way.
Amazon EMR (Elastic MapReduce) presents an effective solution to the otherwise costly affair of managing infinite data. Amazon EMR provides a Hadoop framework for managing big data across Amazon EC2 instances. It is based on Hadoop, a Java-based programming framework that supports the processing of large data sets in a distributed computing environment.
Or in other words, Amazon EMR processes data across a Hadoop cluster of virtual servers on the Amazon Elastic Compute Cloud (EC2). The elastic in EMR’s name refers to its dynamic resizing ability, which allows it to ramp up or reduce resource use depending on the demand at any given time.
Amazon EMR is used for data analysis in log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, bioinformatics and more.
In short EMR is
A framework -> Splits data into pieces -> Let’s processing occur -> Gathers the results
1. Easy to Use
An Amazon EMR cluster can be launched in minutes. It’s hassle-free in terms of node provisioning, cluster setup, Hadoop configuration, or cluster tuning. Amazon EMR takes care of these tasks so that focus on analysis can be given.
2. Low Cost
Amazon EMR pricing is simple and predictable: an hourly rate for every instance hour can be used. A 10-node Hadoop cluster can be launched for as little as $0.15 per hour. Because Amazon EMR has native support for Amazon EC2 Spot and Reserved Instances, 50-80% on the cost can be saved of the underlying instances.
With Amazon EMR, one, hundreds, or thousands of compute instances can be provisioned to process data at any scale. The number of instances can be easily increased or decreased and payment can be made for what is being used.
It takes very less time to tune and monitor the cluster. Amazon EMR has tuned Hadoop for the cloud; it also monitors the cluster —retrying failed tasks and automatically replacing poorly performing instances.
Amazon EMR automatically configures Amazon EC2 firewall settings that control network access to instances, clusters in an Amazon Virtual Private Cloud (VPC) can be launched, a logically isolated network you define. For objects stored in Amazon S3 (all about Amazon S3 outage), Amazon S3 server-side encryption or Amazon S3 client-side encryption with EMRFS can be used, with AWS Key Management Service or customer-managed keys.
Amazon EMR gives complete control over the cluster. It enables root access to every instance so that additional applications can be easily installed, and every cluster can be customized. Amazon EMR also supports multiple Hadoop distributions and applications.
Today, every business is flooded with terabytes and petabytes of information. IT departments are burdened with tight budgets and are expected to make sense of the ever-growing amount of data and help businesses stay ahead. Hadoop and the MapReduce framework have been powerful tools to help in this fight of being ahead. However, these frameworks are costly and time-consuming to build out and maintain vast IT infrastructure to do this work in the traditional data center.
Amazon’s EMR is an in-the-cloud solution that supplies both the computing horsepower and the on-demand infrastructure needed to solve these complex issues of finding trends and understanding vast volumes of data. EMR is here to stay and help relentlessly to the data scientists and businesses in an effective way.