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

Chapter 7: Understanding Security in Amazon EMR

In the previous chapter, you learned about EMR cluster monitoring, scaling, high availability, and cloning capabilities.

When you implement solutions in AWS, security is the most important thing that you should be focusing on. These security aspects include infrastructure security, network security, and data-level security. AWS provides several services and features by means of which you can implement security around your solution.

In this chapter, we will explain how you can control authentication and authorization in relation to your cluster, how you can secure data with encryption at rest and in transit, and finally, how AWS IAM, VPC, subnets, and cluster security groups play a role in making the cluster secure.

Now, let's dive deep into the following topics and understand how they help in implementing security in Amazon EMR:

  • Understanding the basics of security
  • AWS IAM integration with Amazon EMR
  • Understanding...