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Amazon EMR

What is it​

A managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, on AWS.

What it's for​

Processing large volumes of data, big data analytics, machine learning, and running interactive SQL queries at scale.

Use cases​

  • Data processing for ETL (Extract, Transform, Load)
  • Real-time and batch data analysis
  • Machine learning and data processing for ML models
  • Log and clickstream analysis
  • Bioinformatics and genomics

Key points​

  • Managed: AWS manages the cluster infrastructure, including provisioning, configuration, and scaling
  • Big Data Frameworks: Supports a wide range of open-source frameworks, such as Hadoop, Spark, Hive, Presto, Flink, etc.
  • Scalability: Allows elastically scaling clusters to meet processing needs
  • Integration: Integrates with other AWS services, such as Amazon S3 for data storage and Amazon EC2 for compute capacity
  • Cost-effective: Pay only for the capacity you use, with spot instance options to save costs

Comparison​

  • Amazon EMR vs. EC2 (self-managed): EMR simplifies running big data frameworks by abstracting the complexity of managing servers and software, while running these frameworks directly on EC2 requires the user to configure and maintain the entire software stack.
  • Amazon EMR vs. AWS Glue: EMR is more flexible for complex big data workloads that require control over the cluster and the ability to use different frameworks. AWS Glue is a serverless, fully managed ETL service, ideal for simpler and automated data transformations.