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 14: Best Practices and Cost-Optimization Techniques

Welcome to the last chapter of the book! During all the previous chapters, you learned about EMR and its advanced configurations and security. You also learned how you can migrate your on-premise workloads to AWS and how you can implement batch, streaming, or interactive workloads in the AWS cloud. In this chapter, we will focus on some of the best practices and cost optimization techniques you can follow to get the best out of Amazon EMR.

When considering best practices for implementing big data workloads in EMR, we should look at different aspects such as EMR cluster configuration, sizing your cluster, scaling it, applying optimization on S3 or HDFS storage, implementing security best practices, and different architecture patterns. Apart from these, optimizing costs is also a best practice and AWS provides several ways to optimize costs and offers various tools to monitor, forecast, and get notified when your spending...