You're reading from Simplify Big Data Analytics with Amazon EMR A beginner's guide to learning and implementing Amazon EMR for building data analytics solutions

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781801071079

Length 430 pages

Edition 1st Edition

Tools

Amazon EMR

Concepts

Big Data

Author (1):

Sakti Mishra

Preface

1. Section 1: Overview, Architecture, Big Data Applications, and Common Use Cases of Amazon EMR

2. Chapter 1: An Overview of Amazon EMR FREE CHAPTER

3. Chapter 2: Exploring the Architecture and Deployment Options

4. Chapter 3: Common Use Cases and Architecture Patterns

5. Chapter 4: Big Data Applications and Notebooks Available in Amazon EMR

6. Section 2: Configuration, Scaling, Data Security, and Governance

7. Chapter 5: Setting Up and Configuring EMR Clusters

8. Chapter 6: Monitoring, Scaling, and High Availability

9. Chapter 7: Understanding Security in Amazon EMR

10. Chapter 8: Understanding Data Governance in Amazon EMR

11. Section 3: Implementing Common Use Cases and Best Practices

12. Chapter 9: Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark

13. Chapter 10: Implementing Real-Time Streaming with Amazon EMR and Spark Streaming

14. Chapter 11: Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi

15. Chapter 12: Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA

16. Chapter 13: Migrating On-Premises Hadoop Workloads to Amazon EMR

17. Chapter 14: Best Practices and Cost-Optimization Techniques

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Test your knowledge

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

Assume you have several on-premises Hadoop workloads, out of which a few are subject to sensitive customer SLAs, and your organization has decided to move all workloads to AWS. Which migration strategy do you think is ideal for your use case?
Assume you have around 100 petabytes of data in your on-premise environment and you are planning to migrate the data to Amazon S3. Looking at the volume of data, which data migration strategy or tool do you think is best for your use case?
Assume you have completed the migration of your on-premise environment that included several Hadoop workloads and 100s of terabytes of data. Now you are looking for ways to validate the data quality in Amazon S3. Which tool or utility will be helpful to check the quality of the data?

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