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

Implementation steps

In this section, we will guide you through the implementation steps for the use case and architecture we explained in the previous section.

Important Note

Please note, while explaining the implementation steps, we have used us-east-1 as the AWS region. You can use the same or an alternate region as per your choice. Please check any resource or service limits that might apply to your AWS region before proceeding with the implementation.

Creating Amazon S3 buckets

Let's first create the Amazon S3 buckets and folders that will be used for both input and output. Please refer to the following steps to create them:

  1. Navigate to the Amazon S3 console at https://s3.console.aws.amazon.com/s3/home?region=us-east-1#.
  2. From the buckets list, choose Create Bucket, which will open up a form on the web interface to provide your bucket name and related configurations.

We have specified the input bucket name as raw-input and kept everything else...