Book Image

Simplify Big Data Analytics with Amazon EMR

By : Sakti Mishra
Book Image

Simplify Big Data Analytics with Amazon EMR

By: Sakti Mishra

Overview of this book

Amazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS.
Table of Contents (19 chapters)
1
Section 1: Overview, Architecture, Big Data Applications, and Common Use Cases of Amazon EMR
6
Section 2: Configuration, Scaling, Data Security, and Governance
11
Section 3: Implementing Common Use Cases and Best Practices

Technical requirements

In this chapter, we will dive deep into the different security aspects of EMR, including IAM access permissions, data encryption, and controlling network traffic to the EMR cluster. Before getting started, please make sure you have access to the following resources.

  • An AWS account
  • An IAM user who has permission to create and manage an EMR cluster with related resources, including Amazon EC2 instances, required IAM roles, and security groups
  • IAM access privileges to create VPC endpoints as well as create and manage encryption keys using AWS KMS
  • Access to EMR security documentation that is available through

Now, let's understand what it means when we talk about security in EMR and how the shared responsibility model works.