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Data Engineering with AWS

You're reading from   Data Engineering with AWS Acquire the skills to design and build AWS-based data transformation pipelines like a pro

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Length 636 pages
Edition 2nd Edition
Tools
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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 (24) Chapters Close

Preface 1. Section 1: AWS Data Engineering Concepts and Trends
2. An Introduction to Data Engineering FREE CHAPTER 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

Hands-on – Setting up Amazon DataZone

In the hands-on section of this chapter, we are going to set up and configure the Amazon DataZone service. We will then create a DataZone project, import a data source, add business metadata, and publish a data product. Finally, we will access the DataZone data portal as a data consumer, to search for and subscribe to a data product.

At the time of publication of this book, the Amazon DataZone service has just recently been released. There are sometimes a number of changes made to a service shortly after it becomes Generally Available (GA), so make sure to reference the GitHub page for this chapter to check for any updates related to these hands-on exercises. The GitHub page is available at https://github.com/PacktPublishing/Data-Engineering-with-AWS-2nd-edition/tree/main/Chapter15

Let’s get started.

Setting up AWS Identity Center

To log in to the Amazon DataZone data portal, you can either use your AWS IAM credentials...

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