Simplify Big Data Analytics with Amazon EMR

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.

Type
ebook
Category
Data
publication date
2022-03-25
what you will learn

Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio
Configure, deploy, and orchestrate Hadoop or Spark jobs in production
Implement the security, data governance, and monitoring capabilities of EMR
Build applications for batch and real-time streaming data analytics solutions
Perform interactive development with a persistent EMR cluster and Notebook
Orchestrate an EMR Spark job using AWS Step Functions and Apache Airflow

no of pages
430
duration
860
key features
Build data pipelines that require distributed processing capabilities on a large volume of data * Discover the security features of EMR such as data protection and granular permission management * Explore best practices and optimization techniques for building data analytics solutions in Amazon EMR
approach
Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin by exploring EMR features, architecture and use case deep dive with implementation examples.
audience
This book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
meta description
Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services
short description
This book will take you through the Amazon EMR architecture, features, and common use cases or problem statements it solves. You’ll discover how to configure it in production with scaling, monitoring, and security best practices, while also understanding different implementations of batch, real-time streaming, and interactive analytics workloads.
subtitle
A beginner's guide to learning and implementing Amazon EMR for building data analytics solutions
keywords
EMR, Data analytics, big data
Product ISBN
9781801071079