Elastic MapReduce

AWS Elastic MapReduce (EMR): Components and Architecture Guide

Overview

Elastic MapReduce (EMR) is not a single product but rather a collection of open-source projects bundled together in an easily deployable package. It serves as a managed Hadoop framework designed for processing large volumes of data, with support for various open-source technologies including Apache Spark, HBase, Presto, and Flink.

Core Components of the Hadoop Ecosystem

Central Components

1. Hadoop HDFS (Hadoop Distributed File System)

   • Primary storage system

   • Optimized for data analytics and manipulation

   • Forms the foundation of the EMR infrastructure

2. Hadoop MapReduce

   • Core processing framework

   • Handles distributed data processing

   • Gives EMR its name and fundamental functionality

Supporting Components

Data Management and Processing

• HBase

  • Columnar database

  • Specialized for Hadoop data storage

  • Optimized for large-scale data operations

• Pig

  • Scripting framework

  • Simplifies data manipulation tasks

  • Provides high-level programming interface

• Hive

  • SQL interface for Hadoop

  • Enables SQL-like queries on Hadoop data

  • Makes data accessible to SQL-proficient users

Data Ingestion and Integration

• Flume

  • Specialized for log ingestion

  • Handles application and system logs

  • Streamlines data collection

• Sqoop

  • Data import facilitator

  • Connects to external databases

  • Enables data migration into Hadoop

System Management

• ZooKeeper

  • Resource coordination service

  • Ensures proper service integration

  • Manages distributed system configuration

• Oozie

  • Workflow management framework

  • Coordinates job execution

  • Handles task scheduling

Advanced Features

• Mahout

  • Machine learning capabilities

  • Provides predictive analytics

  • Enables advanced data analysis

• Ambari

  • Management and monitoring console

  • Provides system oversight

  • Offers administrative interface

EMR Architecture

Cluster Structure

1. Master Node

   • Controls cluster operations

   • Manages job distribution

   • Coordinates other nodes

2. Core Nodes

   • Contains HDFS storage

   • Provides persistent data storage

   • Essential for data retention

3. Task Nodes

   • Worker nodes for processing

   • Ephemeral storage only

   • Scalable for performance

Processing Steps

EMR processes data through programmatic tasks called steps. Example workflow:

1. Initial data processing (e.g., Hive SQL queries)

2. Custom processing (e.g., Java JAR applications)

3. Data transformation (e.g., Pig scripts)

4. Results storage in S3

Common Use Cases

• Log analysis at scale

• Financial data processing

• ETL (Extract, Transform, Load) operations

• Large-scale data processing

• Anomaly detection in massive datasets

Enterprise Support

Major providers of enterprise support and professional services:

• Hortonworks

• Cloudera

These companies contribute to the open-source projects and provide additional enterprise features and support.

Best Practices

• Design steps to be independent and modular

• Utilize appropriate node types based on workload

• Implement proper data backup strategies

• Monitor cluster performance

• Scale task nodes based on processing needs

Considerations

• EMR simplifies deployment compared to manual setup

• Offers push-button deployment of complex systems

• Provides managed service benefits

• Enables focus on data processing rather than infrastructure

• Scales effectively for large data volumes