Amazon Elastic MapReduce, or Amazon EMR, is a platform that is taking over the world. Companies around the world have realized how important gathering and using data can be for helping them grow. This data, when recognized and analyzed well, can tell businesses which products to release, how to target the right customer, how to beat the competition and more.
Amazon EMR is a useful tool for many businesses to use. It provides these companies with a managed framework using Hadoop, along with the elastic infrastructure of Amazon EC2 and Amazon S3.
Amazon Elastic MapReduce (EMR) is a web service that provides a managed framework to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto in an easy, cost-effective, and secure manner.
Amazon EMR (Amazon Elastic MapReduce) provides a managed Hadoop framework using the elastic infrastructure of Amazon EC2 and Amazon S3.
It distributes computation of the data over multiple Amazon EC2 instances. AWS EMR is easy to use as the user can start with the easy step which is uploading the data to the S3 bucket. After that, the user can upload the cluster within minutes.
Analysis of the data is easy with Amazon Elastic MapReduce as most of the work is done by EMR and the user can focus on Data analysis. The output can retrieve through the Amazon S3.
Amazon Elastic MapReduce – Open Source Applications
These are the popular open source applications use in AWS EMR:
a. Apache Hadoop
Hadoop is used to process large datasets and it is an open source software project. Hadoop diminishes the use of a single large computer. It allows clustering commodity hardware together to analyze massive data sets in parallel.
b. Apache Spark
Apache Spark is used for big data workloads and is an open-source, distributed processing system. It optimizes execution for the fast processing and supports general batch processing streaming analytics, machine learning, and graph databases.
c. Apache HBase
Apache HBase is a large scalable distributed Big Data store which is present in the Hadoop ecosystem. It runs on the top of Amazon S3 or the Hadoop Distributed File System (HDFS). It is loaded with inbuilt access to tables with billions of rows and millions of columns.
Presto helps to process data from various data stores which includes Hadoop Distributed File System (HDFS) and Amazon S3. It is optimized for low-latency, ad-hoc analysis of data.
Benefits of Amazon EMR
Following are the AWS EMR benefits, let’s discuss them one by one:
With the help of Amazon Elastic MapReduce, the user can monitor myriads of compute instances for data processing. Amazon AutoScaling can use to modify the number of instances automatically. Instance modifications can do manually by the user so that the cost may reduce.
AWS EMR is cheap as one can launch 10-node Hadoop cluster for $0.15 per hour. Amazon EMR has a support for Amazon EC2 Spot and Reserved Instances. This helps them to save 50-80% on the cost of the instances.
AWS EC2 has an inbuilt capability to turn on the firewall for the protection and controlling cloud network access to instances. Clusters can also launch in Virtual Private Cloud a logically isolated network for higher security.
While using AWS EMR the user is flexible for performing tasks such as root access to any instance, Installation of additional applications, and customization of the cluster with bootstrap actions.
What Can AWS EMR Perform?
These are the activities, which perform by Amazon Elastic MapReduce :
a. Real-time Analytics
The user can use and process the real-time data. Streaming analytics can perform in a fault tolerant way and the results can be submitted to Amazon S3 or HDFS.
b. Log Analysis
Log processing is easy with AWS EMR and generates by web and mobile application. The unstructured or semi-structured data can also convert into useful insights with the help of Amazon EMR.
c. Clickstream analysis
To deliver more effective and useful advertisements Amazon Elastic MapReduce can use to analyze Clickstream data.
d. Extract Transform Load
AWS EMR often accustoms quickly and cost-effectively perform data transformation workloads (ETL) like – sort, aggregate, and part of – on massive datasets.
e. Predictive Analytics
Apache Spark on AWS EMR includes MLlib for scalable machine learning algorithms otherwise you will use your own libraries. By storing datasets in-memory, Spark will offer nice performance for common machine learning workloads.
AWS EMR, often accustom method immense amounts of genomic data and alternative giant scientific information sets quickly and expeditiously. Researchers will access genomic data hosted for free of charge on Amazon Web Services.
Amazon EMR Components:
1) Clusters – A collection of EC2 instances. You can create two types of clusters:
a transient cluster that auto-terminates after steps complete.
a long-running cluster that continues to run until you terminate it deliberately.
2) Nodes – Each EC2 instance in a cluster is called a node.
Node Type – Each node has a role within the cluster, referred to as the node type. The node types are:
Master node: A node that manages the cluster by running software components to coordinate the distribution of data and tasks among other nodes for processing. The master node tracks the status of tasks and monitors the health of the cluster. Every cluster has a master node, and it’s possible to create a single-node cluster with only the master node. Does not support automatic failover.
Core node: A node with software components that run tasks and store data in the Hadoop Distributed File System (HDFS) on your cluster. Multi-node clusters have at least one core node. EMR is fault tolerant for slave failures and continues job execution if a slave node goes down.
3) Task node: A node with software components that only runs tasks and does not store data in HDFS. Task nodes are optional.
Amazon EMR Pricing:
You pay a per-second rate for every second for each node you use, with a one-minute minimum.
The EMR price is in addition to the EC2 price (the price for the underlying servers) and EBS price (if attaching EBS volumes).