Amazon EMR

You can use EMR Studio, an integrated development environment (IDE), to easily develop, visualize, and debug data engineering and data science applications written in R, Python, Scala, and PySpark. EMR Studio uses AWS Single Sign-On and allows you to log in directly with your corporate credentials. It provides fully managed Jupyter Notebooks and collaboration with peers using code repositories such as GitHub and BitBucket.

EMR pricing is simple and predictable: You pay a per-instance rate for every second used, with a one-minute minimum charge. You can launch a 10-node EMR cluster for as little as $0.15 per hour. You can save 50-80% on the cost of the instances by selecting Amazon EC2 Spot for transient workloads and Reserved Instances for long-running workloads. You can also use Savings Plans.

Unlike the rigid infrastructure of on-premises clusters, EMR decouples compute and storage, giving you the ability to scale each independently and take advantage of the tiered storage of Amazon S3. With EMR, you can provision one, hundreds, or thousands of compute instances or containers to process data at any scale. The number of instances can be increased or decreased automatically using Auto Scaling (which manages cluster sizes based on utilization) and you only pay for what you use.

Spend less time tuning and monitoring your cluster. EMR is tuned for the cloud and constantly monitors your cluster — retrying failed tasks and automatically replacing poorly performing instances. Clusters are highly available and automatically failover in the event of a node failure. EMR provides the latest stable open source software releases, so you don’t have to manage updates and bug fixes, which leads to fewer issues and less effort to maintain your environment.

EMR automatically configures EC2 firewall settings, controlling network access to instances and launches clusters in an Amazon Virtual Private Cloud (VPC). Server-side encryption or client-side encryption can be used with the AWS Key Management Service or your own customer-managed keys. EMR makes it easy to enable other encryption options, like in-transit and at-rest encryption, and strong authentication with Kerberos. You can use AWS Lake Formation or Apache Ranger to apply fine-grained data access controls for databases, tables, and columns.

You have complete control over your EMR clusters and your individual EMR jobs. You can launch EMR clusters with custom Amazon Linux AMIs and easily configure the clusters using scripts to install additional third party software packages. EMR enables you to reconfigure applications on running clusters on the fly without the need to relaunch clusters. Also, you can customize the execution environment for individual jobs by specifying the libraries and runtime dependencies in a Docker container and submit them with your job.

You can deploy EMR on Amazon EC2 and take advantage of On-Demand, Reserved, and Spot Instances. EMR manages provisioning, management, and scaling of the EC2 instances. AWS offers more instance options than any other cloud provider, allowing you to choose the instance that gives you the best performance or cost for your workload.

Learn more »

You can use EMR to run Apache Spark jobs on demand on Amazon Elastic Kubernetes Service (EKS), without needing to provision EMR clusters, to improve resource utilization and simplify infrastructure management. Amazon EKS gives you the flexibility to start, run, and scale Kubernetes applications in the AWS cloud or on-premises. With Amazon EMR on EKS, you can share compute and memory resources across all of your applications and use a single set of Kubernetes tools to centrally monitor and manage your infrastructure.

Learn more »

Amazon EMR is available on AWS Outposts, allowing you to set up, deploy, manage, and scale EMR in your on-premises environments, just as you would in the cloud. AWS Outposts brings AWS services, infrastructure, and operating models to virtually any data center, co-location space, or on-premises facility.

Learn more »

Use EMR's built-in machine learning tools, including Apache Spark MLlib, TensorFlow, and Apache MXNet for scalable machine learning algorithms, and use custom AMIs and bootstrap actions to easily add your preferred libraries and tools to create your own predictive analytics toolset.

EMR can be used to quickly and cost-effectively perform data transformation workloads (ETL) such as sort, aggregate, and join on large datasets.

Analyze clickstream data from Amazon S3 using Apache Spark and Apache Hive to segment users, understand user preferences, and deliver more effective ads.

Analyze events from Apache Kafka, Amazon Kinesis, or other streaming data sources in real-time with Apache Spark Streaming and Apache Flink to create long-running, highly available, and fault-tolerant streaming data pipelines on EMR. Persist transformed data sets to S3 or HDFS and insights to Amazon Elasticsearch Service.

EMR Notebooks provide a managed analytic environment based on open-source Jupyter that allows data scientists, analysts, and developers to prepare and visualize data, collaborate with peers, build applications, and perform interactive analyses.

EMR can be used to process vast amounts of genomic data and other large scientific data sets quickly and efficiently. Researchers can access genomic data hosted for free on AWS.