What Is AWS EMR?

In this article, we’ll learn about Big Data and the tool provided by Amazon – the Amazon EMR. We’ll discuss what it is, the various benefits provided by the Amazon EMR, and also dive into the methods of the deployment with the Amazon EMR. Moreover, we’ll learn about what the Amazon EMR is used for in the vast range of fields and lastly conclude by learning about the tools that are used for Big Data supported by Amazon EMR.

Big Data

Big Data, as the name suggests, refers to a large, difficult-to-manage volume of data that can consist of both structured and unstructured data that are used and produced by businesses on day-to-day activities. These data can be analyzed to extract information such that insights can be obtained to improve decision-making for businesses and so much more. It can be understood as a set of numerous technologies too which are created for storage, analysis, and management of the bulk data which consists of the macro-tools that identify patterns from the chaos of the data for creative solutions that can change domains from agriculture to medicine.

What is AWS EMR?

AWS EMR or Amazon EMR is Amazon’s offering which is now regarded as the leader in the industry for a big data platform that can process vast amounts of data through numerous open-source tools from Apache Spark, Apache HBase, Apache Hive, Apache Hudi, Presto, and Apache Flink.

Benefits of AWS EMR

Effortless Use Case

The EMR Studio makes it extremely easy with its integrated development environment to develop, visualize and debug various applications of data science projects as well as data engineering. It can address programs written in Python, R, PySpark, and Scala. Moreover, the EMR Studio makes use of the AWS Single Sign-On which enables logging in simply through the corporate credentials. Through the code repositories like BitBucket and GitHub collaboration can be done with peers and also full support is provided for Jupyter Notebook.

Economic Pricing

The pricing of EMR is extremely economic. It can be easily calculated too and predicted for the possible expenditure. The pay as per the instance rate makes it easier to figure out the possible charge as per the usage. A 10-node EMR cluster can be launched for as low as $0.15 per hour. Moreover, up to 80% of the cost for the instances are saved using the Amazon EC2 Sport for workloads of transient nature and Reserved instances for workloads that are long-running.


The tiered storage of Amazon S3 can be made use of and contrary to the rigid systems of on-premises, EMR helps to decouple the compute and storage thus making it easy to scale each component independently. Instances and containers can be scaled from one to thousands in order to process data with so much ease. Furthermore, the change in the number of instances can be performed automatically for both increasing and scaling through the Auto Scaling feature that is based on the utilization in order to manage the size of the clusters while only paying for what is used. The scalability and elasticity make Amazon EMR exceptionally lucrative to use.


With Amazon EMR, minimal time would be required to spend for monitoring and tuning the clusters. Clusters are constantly monitored in order to retry the failed tasks and replacing the instances that are poorly performing automatically. We can realize how reliable Amazon EMR is as it has clusters that are highly available and has automatic failover for the cases of node failure events. Moreover, the latest stable releases are updated by the system itself mitigating the requirement of constant management, updating, and fixing of the bugs for the users to maintain the environment. 


EC2 firewall settings is configured in the Amazon EMR with network access to instances and clusters in Amazon Virtual Private Cloud thoroughly controlled. The AWS Key Management Service and Customer Managed Keys are enabled with server-side encryption and client-side encryption respectively. Moreover, numerous other encryptions to are provided by the EMR such as in-transit and at-rest encryptions and strong authentication supported by Kerberos. Moreover, Apache Ranger and AWS Lake Formation can be used in order to apply the finely-grained data access controls over the tables, columns, and databases. Overall, the security of big data is completely taken care of.


Amazon EMR is extremely flexible. It provides complete control over the EMR clusters and the individual EMR jobs to its users. Also, the EMR clusters can be launched with the custom Amazon Linux AMIs for clusters which can also be conveniently configured through the scripts for the installation of any additional third-party software packages. Moreover, the applications can be reconfigured on the fly without any requirement of relaunching the clusters. Lastly, the execution environment can be easily customized for the individual jobs by simply specifying the runtime dependencies and libraries in the Docker container when submitting the job or tasks. 

Methods for Deployment

The Amazon EMR can be deployed with numerous other services provided by AWS. These are listed as described below. 

Amazon EC2

Amazon EC2 stands for Amazon Elastic Compute Cloud which provides different instance types for elastic compute with security, resizability, and compute capacity. Numerous features such as on-demand, reserved and spot instances can be taken advantage of with the deployment of the EMR on the Amazon EC2. From provisioning, management, as well as scaling of the EC2 instances, can be managed with the EMR. With the plethora of instances offered by AWS, optimum value with outstanding performance for the lowest of costs for the workloads can be obtained.

Amazon EKS

The Amazon EKS provides supports with different capabilities dedicated to the Kubernetes application. Apache Spark jobs can be run on-demand on the Amazon Elastic Kubernetes Service (EKS) with the Amazon EMR without any requirement of the provisioning of EMR clusters. Kubernetes applications can be started, run, and scaled in the AWS cloud as well as on-premises with Amazon EKS. Moreover, compute and memory resources can also be shared across the applications using a unified set of Kubernetes tools that can help monitor and manage all the infrastructure centrally.

AWS Outposts

AWS Outposts in simple words can be understood as the service offered by AWS which can extend the AWS infrastructure, APIs, tools, and the different AWS services out there. AWS Outposts enables Amazon EMR. Setting up, deployment, management, and scaling of EMR can be done easily in the cloud as well as the on-premises environment. Moreover, the AWS Outposts also enables a plethora of other AWS services, infrastructures, and numerous operating models to every virtually possible data center, on-premises facilities, and co-locations space.

What can Amazon EMR be used for?

The Amazon EMR can be used for numerous scenarios and a vast range of goals.

First and foremost, as a tool for Big Data Technologies, performing data transformation workloads of Extract, Transform, and Load (ETL) can be performed for sorting, aggregation, and joining of the large datasets. Secondly, the budding field of Machine Learning is enabled with this huge built-in machine learning tools in EMR such as Apache Spark MLlib, Apache MXNet, and TensorFlow for scaling machine learning algorithms and usage of custom AMIs and bootstrap actions. This will help to add libraries and tools of choice to create a predictive analytics toolset of our own.

Besides, it can support Real-time streaming services with events from Apache Kakka, Amazon Kinesis, and numerous other streaming data sources in real-time. The Apache Spark Streaming and Apache Flink are highly available and fault-tolerant making is suitable even for disaster recovery, in addition, to support for the creation of long-running streaming data pipelines. Moreover, with EMR Notebooks data scientists, analysts and developers are enabled with interactive analytics creation for all the support needed for preparation and visualization of data, collaboration with peers, and applications building. Furthermore, ClickStream Analysis and Genomics can equally benefit from Amazon EMR. Amazon EMR is at the forefront for interactive analytics with all these tools it provides and supports such as EMR Studio, Hue, Jupyter Notebook, Apache Zeppelin, and many more.

Tools for Big Data

Numerous tools of big data used for Machine Learning and Data Processing such as Apache Spark, Apache Flink, TensorFlow, Apache Hudi, and SQL like Apache Hive, Presto, Apache Phoenix are all supported by Amazon EMR. Also, NoSQL such as Apache HBase is enabled by Amazon EMR. Moreover, Amazon EMR clusters with GPU enable defining, training, and deployment of deep neural networks such as the Apache MXNet framework.


Thus, in this article, we learned about Big Data and an overview of the Amazon EMR. We learned what it is, its benefits, and various methods of deployment. Furthermore, we also dived into the fields it can be used to solve different types of problems and then learned about the range of tools and features supported by the Amazon EMR.