An Introduction to Elastic MapReduce: The Basics
Whether your business crunches digital television data concerning viewing behavior, analyzes massive amounts of mechanical sensor data, or analyzes retail shopper behavior, you have huge amounts of data available to you to use in new and innovative ways. That’s the promise of big data.
Elastic MapReduce makes the promises of big data available to more organizations.
Organizations of all types pull in data from a variety of sources, analyze it, and produce valuable, actionable results. Traditionally, this has been done on Hadoop clusters. Hadoop is an open source software framework for processing large scale data sets on “clusters” of hardware. Using Hadoop clusters requires knowing how to configure job management nodes and worker nodes into a cluster, and then configuring and managing that cluster. But Amazon’s Elastic MapReduce lets you put Hadoop in the cloud.
What Is Elastic MapReduce?
Elastic MapReduce is built on the Amazon cloud platform, which many businesses use for computing, networking, storage, database functions, and applications such as payments, email, and push messaging. Elastic MapReduce uses Amazon’s Elastic Compute Cloud generation 2 technology (EC2) for computing, and Amazon’s S3 storage to provision Hadoop clusters in the cloud without having to deal with setup and configuration.
Elastic MapReduce pulls data from S3 and processes it with an automatically configured Hadoop cluster that runs on EC2. When companies use the service, they only pay for the services they use. Simply put, it’s Hadoop as a service.
How Does Elastic MapReduce Work?
Using Elastic MapReduce is basically a five-step process.
There are two main components to Elastic MapReduce: the map function and the reduce function. The map function creates or processes input data, generating the necessary number of outputs for the particular application. The output data from the map function is fed to the reduce function, which processes the data and creates useful information from it. To use Elastic MapReduce you follow a five-step process:
- Develop your data processing application
- Upload your application and data to storage (S3)
- Configure and launch your cluster
- Monitor your cluster
- Retrieve your output from S3 or the Hadoop Distributed File System (HDFS) on the cluster
What Is Elastic MapReduce Used For?
Elastic MapReduce can be used for a practically unlimited range of applications, from log analysis to web indexing to financial analysis to machine learning. It could be used for sentiment analysis about a particular topic, blog, or political candidate, or could be used by a retailer to analyze click stream data to better understand consumer preferences. Advertisers could use click stream analysis and logs of advertising impressions to make more effective advertisements.
There are many scientific and technical uses for Elastic MapReduce too. Huge scientific data sets or genomic data can be analyzed, for example. Web or mobile application makers could use Elastic MapReduce to process logs generated by apps, turning huge amounts of unstructured data into useful information.
Hadoop is an amazing tool, but not every organization has the resources and expertise necessary to configure Hadoop clusters. Elastic MapReduce lets organizations focus on designing the map and reduce workflow rather than spending a lot of time and resources getting the cluster set up and configured. Amazon is one of the world’s biggest Hadoop operators, with customers having launched over 5.5 million Hadoop clusters since Elastic MapReduce was launched. This technology from Amazon empowers more commercial and research organizations to access the fruits of the promise of big data.
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