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Data Engineering with AWS

You're reading from   Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800560413
Length 482 pages
Edition 1st Edition
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Author (1):
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Gareth Eagar Gareth Eagar
Author Profile Icon Gareth Eagar
Gareth Eagar
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: AWS Data Engineering Concepts and Trends
2. Chapter 1: An Introduction to Data Engineering FREE CHAPTER 3. Chapter 2: Data Management Architectures for Analytics 4. Chapter 3: The AWS Data Engineer's Toolkit 5. Chapter 4: Data Cataloging, Security, and Governance 6. Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
7. Chapter 5: Architecting Data Engineering Pipelines 8. Chapter 6: Ingesting Batch and Streaming Data 9. Chapter 7: Transforming Data to Optimize for Analytics 10. Chapter 8: Identifying and Enabling Data Consumers 11. Chapter 9: Loading Data into a Data Mart 12. Chapter 10: Orchestrating the Data Pipeline 13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
14. Chapter 11: Ad Hoc Queries with Amazon Athena 15. Chapter 12: Visualizing Data with Amazon QuickSight 16. Chapter 13: Enabling Artificial Intelligence and Machine Learning 17. Chapter 14: Wrapping Up the First Part of Your Learning Journey 18. Other Books You May Enjoy

Examining the options for orchestrating pipelines in AWS

As you will have noticed throughout this book, AWS offers many different building blocks for architecting solutions. When it comes to pipeline orchestration, AWS provides native serverless orchestration engines with AWS Data Pipeline and AWS Step Function, a managed open source project with Amazon Managed Workflows for Apache Airflow (MWAA), and service-specific orchestration with AWS Glue Workflows.

There are pros and cons to using each of these solutions, depending on your use case. And when you're making a decision, there are multiple factors to consider, such as the level of management effort, the ease of integration with your target ETL engine, logging, error handling mechanisms, and cost and platform independence.

In this section, we'll examine each of the four pipeline orchestration options.

AWS Data Pipeline for managing ETL between data sources

AWS Data Pipeline is one of the oldest services that...

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