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

Summary

Over the course of this chapter, we have learned about recommendations around choosing between transient and persistent clusters, how you can right-size your cluster with different EC2 instance types, and EC2 pricing models. We have also provided best practices around EMR cluster configurations that included cluster scaling, high availability, monitoring, tagging, catalog management, persistent storage, and security best practices.

Then, later in the chapter, we covered cost-optimization techniques that included recommendations around compute and storage, and also covered different tools AWS offers, such as AWS Cost Explorer, AWS Trusted Advisor, and cost allocation tags to monitor and control your costs with alarm notifications with AWS Budgets.

That concludes this chapter and, with it, we have reached the end of the book! Hopefully, this book has helped you to get deep knowledge of EMR's features, usage, integration with other AWS services, on-premise migration...