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Simplify Big Data Analytics with Amazon EMR

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

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801071079
Length 430 pages
Edition 1st Edition
Concepts
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Author (1):
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Sakti Mishra Sakti Mishra
Author Profile Icon Sakti Mishra
Sakti Mishra
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Table of Contents (19) Chapters Close

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 18. Other Books You May Enjoy

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

In the previous few chapters, we explained how you can leverage the EMR cluster for on-demand ETL jobs or long-running clusters that either execute a real-time streaming application or serve as a backend for interactive development using notebooks. But when we build a data pipeline to automate data ingestion, cleansing, or transformations, we look for orchestration tools with which we can build workflows that either get kicked off through a schedule or through an event.

There are two primary orchestration tools – AWS Step Functions and Apache Airflow, which are very popular in building data pipelines with Amazon EMR. AWS also provides a managed offering of Airflow, called Amazon Managed Workflows for Apache Airflow (MWAA).

In this chapter, we will provide an overview of AWS Step Functions and MWAA services and then explain how you can leverage them to orchestrate a data pipeline that...

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