English | 2022 | ISBN: ‎ 1801071071 | 430 pages | True PDF EPUB | 23.83 MB

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 EMR

Book Description

EMR, formerly Elastic MapReduce, provides a managed Hadoop cluster in Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS.

This book is a practical guide to EMR for building data pipelines. You'll start by understanding the 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 EMR use cases, including batch ETL with Spark, real- 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 EMR and also migrate your existing on-premises Hadoop workloads to AWS.

What you will learn

Explore 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- 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 eeers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and 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 EMR

Exploring the Architecture and Deployment Options

Common Use Cases and Architecture Patterns

Big Data Applications and Notebooks Available in EMR

Setting Up and Configuring EMR Clusters

Monitoring, Scaling, and High Availability

Understanding Security in EMR

Understanding Data Governance in EMR

Implementing Batch ETL Pipeline with EMR and Apache Spark

Implementing Real- Streaming with EMR and Spark Streaming

Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi

Orchestrating EMR Jobs with AWS Step Functions and Apache Airflow/MWAA

Migrating On-Premises Hadoop Workloads to EMR

Best Practices and Cost Optimization Techniques