Simplify Big Data Analytics with Amazon EMR: A beginner's guide to learning and implementing Amazon EMR for building data analytics solutions
Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services
- 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
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.
1. An Overview of Amazon EMR
2. Exploring the Architecture and Deployment Options
3. Common Use Cases and Architecture Patterns
4. Big Data Applications and Notebooks Available in Amazon EMR
5. Setting Up and Configuring EMR Clusters
6. Monitoring, Scaling, and High Availability
7. Understanding Security in Amazon EMR
8. Understanding Data Governance in Amazon EMR
9. Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark
10. Implementing Real-Time Streaming with Amazon EMR and Spark Streaming
11. Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi
12. Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA
13. Migrating On-Premises Hadoop Workloads to Amazon EMR
14. Best Practices and Cost Optimization Techniques