Simplify Big Data Analytics with Amazon EMR

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

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
Book Description
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.

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
Who this book is for
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.

Table of Contents
An Overview of Amazon EMR
Exploring the Architecture and Deployment Options
Common Use Cases and Architecture Patterns
Big Data Applications and Notebooks Available in Amazon EMR
Setting Up and Configuring EMR Clusters
Monitoring, Scaling, and High Availability
Understanding Security in Amazon EMR
Understanding Data Governance in Amazon EMR
Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark
Implementing Real-Time Streaming with Amazon EMR and Spark Streaming
Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi
Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA
Migrating On-Premises Hadoop Workloads to Amazon EMR
Best Practices and Cost Optimization Techniques