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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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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

Business and technical data catalogs

You have probably heard about swamps, even if you have never actually been to one. Generally, swamps are known to be wet areas that smell pretty bad, and where some trees and other vegetation may grow, but the area is generally not fit to be used for most purposes (unless, of course, you're an ogre similar to Shrek, and you make your home in the swamp!).In contrast to a swamp, when most people think about a lake, they picture beautiful scenery with clean water, a beautiful sunset, and perhaps a few ducks gently floating on the water. Most people would hate to find themselves in a swamp if they thought they were going to visit a beautiful lake.In the world of data lakes, as a data engineer, you want to provide an experience that is much like the pure and peaceful lake described previously, and you want to avoid your users finding that the lake looks more like a swamp. However, if you're not careful, your data lake can become a data swamp,...

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