Amazon Elastic MapReduce

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 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 ¥0.187 per hour. You can save the cost of the instances by selecting Amazon EC2 Spot for transient workloads and Reserved Instances for long-running workloads.

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 Amazon 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 Amazon 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.

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.

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 Amazon Web Services.