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Amazon Elastic MapReduce

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  • Useful Links:

  • Exam Tips:

    • Used for big data processing, manipulation, analytics, indexing, transformation, and more (used as a part of data pipeline).
    • If these are mentioned in the exam, EMR is probably being used:
      • Spark
      • HBase
      • Presto
      • Fink
      • Hive
      • Pig
    • EMR deployment options:
      • Cluster for specific function.
      • Create cluster to be long lasting for multiple workloads.
      • Cluster to be connected to and used interactively.
        • Run SQL like queries against hive.
    • Using S3 backed storage allows persistence of the data beyond the life of the cluster.
    • By default all instances in a cluster are place within the same AZ for performance.
    • Try to understand what Spark, Hive, and Pig do.
    • EMR Architecture:
      • Each cluster has at least 1 node - the master node.
        • Master node manages the cluster and its health.
        • It distributes workloads, and acts as the NAME node within MapReduce.
        • The node you SSH into.
      • Clusters can have zero or more core nodes.
        • Act as data nodes for HDFS.
        • Run task trackers and can run mapping and reduce tasks in the cluster.
      • Task nodes are optional:
        • They have no HDFS involvement.
        • They don't run task trackers (core), only run tasks.
        • Ideal for SPOT based scaling.
    • EMR Cost and Performance Optimization:
      • Performance:
        • Launch the cluster as close to the data source as possible, in the same region is preferable.
      • Cost:
        • Use latest generations of the instance type.
        • Only use reserved instances when you know the usage upfront.
        • If you are using per-second billing, it might make more sense to run more instances for a shorter amount of time to process jobs quicker.
          • The logic behind this is that since the hour minimum billing is no longer applicable, you can use more instances to process a job quicker and pay the same price or less.