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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Code in Action
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: Overview, Architecture, Big Data Applications, and Common Use Cases of Amazon EMR
Section 1: Overview, Architecture, Big Data Applications, and Common Use Cases of Amazon EMR
Free Chapter
2
Chapter 1: An Overview of Amazon EMR
Chapter 1: An Overview of Amazon EMR
What is Amazon EMR?
Benefits of Amazon EMR
Decoupling compute and storage
Integration with other AWS services
EMR release history
Comparing Amazon EMR with AWS Glue and AWS Glue DataBrew
Summary
Test your knowledge
Further reading
3
Chapter 2: Exploring the Architecture and Deployment Options
Chapter 2: Exploring the Architecture and Deployment Options
EMR architecture deep dive
Understanding clusters and nodes
Using S3 versus HDFS for cluster storage
Understanding the cluster life cycle
Building Hadoop jobs with dependencies in a specific EMR release version
EMR deployment options
Summary
Test your knowledge
Further reading
4
Chapter 3: Common Use Cases and Architecture Patterns
Chapter 3: Common Use Cases and Architecture Patterns
Reference architecture for batch ETL workloads
Reference architecture for clickstream analytics
Reference architecture for interactive analytics and ML
Reference architecture for real-time streaming analytics
Reference architecture for genomics data analytics
Reference architecture for log analytics
Summary
Test your knowledge
Further reading
5
Chapter 4: Big Data Applications and Notebooks Available in Amazon EMR
Chapter 4: Big Data Applications and Notebooks Available in Amazon EMR
Technical requirements
Understanding popular big data applications in EMR
Machine learning frameworks available in EMR
Notebook options available in EMR
Summary
Test your knowledge
Further reading
6
Section 2: Configuration, Scaling, Data Security, and Governance
Section 2: Configuration, Scaling, Data Security, and Governance
7
Chapter 5: Setting Up and Configuring EMR Clusters
Chapter 5: Setting Up and Configuring EMR Clusters
Technical requirements
Setting up and configuring clusters with the EMR console's quick create option
Advanced configuration for cluster hardware and software
Working with AMIs and controlling cluster termination
Troubleshooting and logging in your EMR cluster
Summary
Test your knowledge
Further reading
8
Chapter 6: Monitoring, Scaling, and High Availability
Chapter 6: Monitoring, Scaling, and High Availability
Technical requirements
Monitoring your EMR cluster
Scaling cluster resources
Cluster cloning and high availability with multiple master nodes
Summary
Test your knowledge
Further reading
9
Chapter 7: Understanding Security in Amazon EMR
Chapter 7: Understanding Security in Amazon EMR
Technical requirements
Understanding the basics of security
AWS IAM integration with Amazon EMR
Understanding data protection in EMR
Role of security groups and interface VPC endpoints
Summary
Test your knowledge
Further reading
10
Chapter 8: Understanding Data Governance in Amazon EMR
Chapter 8: Understanding Data Governance in Amazon EMR
Technical requirements
Understanding data catalog and access management options
Understanding Amazon EMR integration with AWS Lake Formation
Understanding Amazon EMR integration with Apache Ranger
Summary
Test your knowledge
Further reading
11
Section 3: Implementing Common Use Cases and Best Practices
Section 3: Implementing Common Use Cases and Best Practices
12
Chapter 9: Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark
Chapter 9: Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark
Technical requirements
Use case and architecture overview
Implementation steps
Validating the output using Amazon Athena
Spark ETL and Lambda function code walk-through
Summary
Test your knowledge
Further reading
13
Chapter 10: Implementing Real-Time Streaming with Amazon EMR and Spark Streaming
Chapter 10: Implementing Real-Time Streaming with Amazon EMR and Spark Streaming
Technical requirements
Use case and architecture overview
Implementation steps
Validating output using Amazon Athena
Spark Streaming code walk-through
Summary
Test your knowledge
Further reading
14
Chapter 11: Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi
Chapter 11: Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi
Technical requirements
Apache Hudi overview
Creating an EMR cluster and an EMR notebook
Interactive development with Spark and Hudi
Summary
Test your knowledge
Further reading
15
Chapter 12: Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA
Chapter 12: Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA
Technical requirements
Overview of AWS Step Functions
Integrating AWS Step Functions to orchestrate EMR jobs
Overview of Apache Airflow and MWAA
Integrating Airflow to trigger EMR jobs
Summary
Test your knowledge
Further reading
16
Chapter 13: Migrating On-Premises Hadoop Workloads to Amazon EMR
Chapter 13: Migrating On-Premises Hadoop Workloads to Amazon EMR
Understanding migration approaches
Migrating data and metadata catalogs
Migrating ETL jobs and Oozie workflows
Testing and validation
Best practices for migration
Summary
Test your knowledge
Further reading
17
Chapter 14: Best Practices and Cost-Optimization Techniques
Chapter 14: Best Practices and Cost-Optimization Techniques
Best practices around EMR cluster configurations
Optimization techniques for data processing and storage
Security best practices
Cost-optimization techniques
Limitations of Amazon EMR and possible workarounds
Summary
Test your knowledge
Further reading
Why subscribe?
18
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Test your knowledge

Before moving on to the next chapter, test your knowledge with the following questions:

  1. Assume that as part of your EMR cluster, you have some custom applications running that will be interacting with AWS services directly instead of executing Hadoop or Spark jobs. Your custom application needs to authenticate itself with AWS IAM to interact with the AWS services and should also have required privileges. How would you enable your application to authenticate itself with AWS IAM to get temporary credentials for access?
  2. Assume that you are using Amazon S3 as your persistent data store in EMR and your organization has strict security rules to encrypt all the data you store. You have your own custom encryption keys that need to be used to encrypt your data. How would you ensure that EMR uses your custom key to encrypt data at rest?
  3. Assume that you have an EMR notebook that needs to push or pull code from the GitHub repository and you have required IAM...
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