Simplify Big Data Analytics with Amazon EMR

A beginner's guide to learning and implementing Amazon EMR for building data analytics solutions

By: Sakti Mishra

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eText | 22 August 2504 | Edition Number 1

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Design scalable big data solutions using Hadoop and AWS cloud native services

Key Features

Build data pipelines that require distributed processing capabilities on a large volume of data
Learn about the security features of EMR such as data protection and granular permission management
Explore best practices for building data analytics pipelines in Amazon EMR

Book Description

Amazon EMR earlier known as Amazon Elastic MapReduce provides a managed Hadoop cluster in AWS which 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, deployment options, and pricing for them. Next, the book covers different big data applications EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and different SDKs/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-premise 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/Spark-based apps on Amazon EMR and also migrate your existing on-premise Hadoop workloads to AWS.

What you will learn

Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio
Configure, deploy, and orchestrate Hadoop/Spark jobs in production
Implement the security, data governance, and monitoring capabilities of EMR
Build applications for batch and streaming data analytics pipelines
Create an interactive environment with Apache Spark and Apache Hudi
Trigger an EMR Spark job using Apache Airflow and Amazon Managed Workflow

Who This Book Is For

This book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data pipelines with the Hadoop ecosystem and AWS services. Prior experience in either Python programming, Scala, or the Java programming language will be beneficial to help you make the most out of this book.