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Hadoop in the cloud with AWS' EMR


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Quick intro to and walkthrough of the AWS Elastic Map Reduce (EMR) service. Part of a larger course at

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Hadoop in the cloud with AWS' EMR

  1. 1. Hadoop in the Cloud: AWS Elastic Map Reduce • What is EMR? • How does EMR compare to Hadoop? • Use cases
  2. 2. EMR is an AWS Service • AWS review helpful to understand • Infiniteskills offers a course! – • AWS constantly changing and evolving
  3. 3. EMR Overview • Abstracts out cluster setup & management – Integrated provisioning, tooling, debug, monitoring – AWS constantly tuning and optimizing – Failed nodes automatically re-provisioned by AWS • Reduced costs – Clusters shut down automatically by default – Excellent for sporadic MapReduce needs • Integration to AWS – Leverage cost-effective EC2 instances for processing, S3 for storage – Monitoring done via CloudWatch
  4. 4. EMR Architecture Master Instance Group EC2 S3 Core Instance Group EC2EC2 HDFS HDFS Task Instance Group EC2 EC2 EC2 EC2 • Master group controls cluster • Core group runs DataNode & TaskTracker daemons • Task group runs tasks • Can be added & removed • S3 can be used for data input / output • Master group coordinates core + task activities and manages cluster state • Core + task instances read / write to / from S3
  5. 5. EMR AWS Integration • Datastore pull / push to – RDS – DynamoDB – S3 • Derived data can be stored in RedShift – Via AWS DataPipelines – Further post-processing • Data can be pre-processed with Kinesis
  6. 6. What you give up with EMR • Control – Always 2-3 months behind Hadoop releases – Cannot use CDH or HDP releases (although MapR is supported) • Speed (if you’re not an AWS customer) • Vendor lock-in
  7. 7. EMR Use Cases • Already AWS customer – Lots of data in S3 / DynamoDB / RDS • Sporadic MapReduce needs • Proof-of-concepting Hadoop • Ease of use – Seamless, near-infinite scale – Simple administration
  8. 8. Hadoop in the Cloud: AWS Elastic Map Reduce • What is EMR? • How does EMR compare to Hadoop? • Benefits & downsides • Use cases