What is Amazon EMR? Data processing frameworks layer?

What is Amazon EMR? Data processing frameworks layer? According to Statista, the mass volume of data created, stored, copied, and consumed in 2020 was over 64 zettabytes (ZB), or about 64 trillion gigabytes (GB). This is expected to rise to 181 ZB by the year 2025.

A large portion of this data is likely to be significant to your business. It can provide you with new insights that help you improve your product, communicate with consumers, and perform risk analysis. However, you’ll need the right tools to extract, sort, process, and analyze it.

That’s where tools like Amazon’s Elastic MapReduce (EMR) come in. In this guide, we’ll discuss what EMR is, how it works, and how it may benefit you. You’ll then be able to decide if it’s worth integrating as part of your big data strategy.

What is Amazon EMR?

Amazon Elastic MapReduce provides tools and workflows for big data management in the cloud. With Amazon EMR, your data scientists get a web-based big data platform that can process massive amounts of data using a variety of open-source tools such as Presto, Apache Spark, and Apache Hive.

EMR also enables you to more easily build, scale, and optimize your cloud data environment compared to building and maintaining one on-premises. Here’s the thing:

Companies seeking to gain more insight and value from their data often struggle to capture, store, and analyze all of it. As data grows, it comes from more sources and becomes increasingly diverse. Thus, it needs to be securely accessed to be analyzed by different applications and lines of business.

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AWS EMR can help solve these issues. EMR is a managed cluster platform that assists organizations in running Big Data frameworks on AWS to analyze and process large sets of data more efficiently.

By using these frameworks along with related opensource projects such as Apache Flink and Apache Pig, you can process and sort data for business intelligence and analytics purposes.

In addition, you can use AWS EMR to transform and move large sets of data into and out of other AWS data stores and databases such as Amazon Simple Storage Services (Amazon S3) and Amazon DynamoDB.

Amazon EMR

Amazon EMR Features: What Can EMR Do?

AWS designed EMR to be an easy-to-use, highly scalable, and reliable big data platform. It does that by enabling certain capabilities, such as:

  • Managed big data platform – Provision, configure, and launch your clusters in minutes by eliminating a lot of the manual work it would otherwise take.
  • Automated elasticity – Use custom policies to continuously scale your clusters so you can meet your workload requirements.
  • Optimize big data processing costs – Deploy multiple clusters or resize a running one to handle an increase in workload or reduce capacity if there’s less work to do, thereby reducing your costs.
  • Leverage a variety of flexible data stores – Use data stores like the Hadoop Distributed File System (HDFS), Amazon DynamoDB, Amazon RedShift, and Amazon Relational Database Service (Amazon RDS).
  • Take advantage of your favorite big data solutions – Select and use the latest version of your choicest open-source platform such as Apache Spark or Hadoop applications.
  • Manage your data with Amazon S3 – Use Apache Hudi to manage incremental data processing and pipeline development.
  • Processing large data sets fast – EMR uses in-memory, fault-tolerant resilient distributed datasets (RDDs) along with directed, acyclic graphs (DAGs) to specify how the data transformations happen.
  • Secure your data with access controls – Amazon EMR application processes call other AWS services using the EC2 instance profile by default. There are three ways Amazon EMR manages access to Amazon S3 data in multi-tenant clusters; by integrating with AWS Lake
  • Formation, integrating natively with Apache Ranger, or with User Role Mapper.
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These features make Amazon EMR ideal for performing big data analytics, building scalable data pipelines, and processing streaming data in real-time. Yet, those are only a few highlighted Amazon EMR features, there are other ways to use the managed big data platform.

Cluster resource management layer

This is where cluster resources are managed. The EMR service uses Yet Another Resource Negotiator (YARN) to centrally manage resources for multiple data processing frameworks. The layer also schedules jobs for processing.

Amazon EMR

Data processing frameworks layer

This is where the data processing and analyses happen using a variety of supported frameworks. So, you can pick a framework based on your processing requirement, such as batch, streaming, interactive, or in-memory. The two main supported frameworks are Hadoop MapReduce and Apache Spark.

App and programs layer

This is where your apps are hosted, including Apache Hive and Pig. The applications let add capabilities such as building data warehouses, using ML algorithms, and creating stream processing apps.

As for how the Amazon EMR architecture works in practice, consider Amazon EMR on Amazon Elastic Kubernetes Service (EKS), as an example.

EMR on EKS loosely couples workloads to the infrastructure they run on. Each infrastructure layer supports orchestration for the following layer.

You first set up Amazon EMR on EKS. Then you assign a job to Amazon EMR through a job definition. A job run is a unit of work, such as a SparkSQL query. The job’s definition includes all of the parameters specific to the application. EKS uses these parameters to determine which pods and containers to deploy.

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How Does Amazon EMR Actually Work?

The Amazon EMR service processes your data using Amazon Elastic Compute Cloud (Amazon EC2) instances along with open-source tools such as Apache Spark, Flink, HBase, and Presto.

You get to pull all data into a data lake and analyze it with your choice of open-source distributed processing frameworks such as:

  • Apache Spark
  • Apache Hadoop
  • Apache Storm
  • Presto

By far, the most popular storage infrastructure for a data lake is Amazon S3. EMR allows you to store data in Amazon S3 and run compute as you need to process that data. EMR clusters can be launched in minutes. You don’t have to worry about node provisioning, cluster setup, Hadoop configuration, or cluster tuning.

Once the processing is done, you can switch off your clusters. You can also automatically resize clusters to accommodate peaks and scale them down without impacting your Amazon S3 data lake storage.

Additionally, you can run multiple clusters in parallel, allowing them to share the same data set. EMR will monitor your clusters, retry failed tasks, and automatically replace poorly performing instances.

If you use Amazon Cloudwatch along with EMR, you can collect and track metrics, logs, and audits. This approach also allows you to set alarms and automatically react to changes.

Amazon EMR

Above is information about What is Amazon EMR? Data processing frameworks layer? that we have compiled. Hopefully, through the above content, you have a more detailed understanding of Amazon EMR. Thank you for reading our post.

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