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Data Engineering with AWS - Second Edition

You're reading from  Data Engineering with AWS - Second Edition

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Pages 636 pages
Edition 2nd Edition
Languages
Author (1):
Gareth Eagar Gareth Eagar
Profile icon Gareth Eagar

Table of Contents (24) Chapters

Preface 1. Section 1: AWS Data Engineering Concepts and Trends
2. An Introduction to Data Engineering 3. Data Management Architectures for Analytics 4. The AWS Data Engineer’s Toolkit 5. Data Governance, Security, and Cataloging 6. Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations
7. Architecting Data Engineering Pipelines 8. Ingesting Batch and Streaming Data 9. Transforming Data to Optimize for Analytics 10. Identifying and Enabling Data Consumers 11. A Deeper Dive into Data Marts and Amazon Redshift 12. Orchestrating the Data Pipeline 13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
14. Ad Hoc Queries with Amazon Athena 15. Visualizing Data with Amazon QuickSight 16. Enabling Artificial Intelligence and Machine Learning 17. Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World
18. Building Transactional Data Lakes 19. Implementing a Data Mesh Strategy 20. Building a Modern Data Platform on AWS 21. Wrapping Up the First Part of Your Learning Journey 22. Other Books You May Enjoy
23. Index

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 Functions, 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. When making a decision on this, 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, cost, and platform independence.

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

AWS Data Pipeline (now in maintenance mode)

AWS Data Pipeline is one of the oldest services that AWS has for creating...

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