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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

Chapter 14: Wrapping Up the First Part of Your Learning Journey

In this book, we have explored many different aspects of the data engineering role by learning more about common architecture patterns, understanding how to approach designing a data engineering pipeline, and getting hands-on with many different AWS services commonly used by data engineers (for data ingestion, data transformation, and orchestrating pipelines).

We examined some of the important issues surrounding data security and governance and discussed the importance of a data catalog to avoid a data lake turning into a data swamp. We also reviewed data marts and data warehouses and introduced the concept of a data lake house.

We learned about data consumers – the end users of the product that's produced by data engineering pipelines – and looked into some of the tools that they use to consume data (including Amazon Athena for ad hoc SQL queries and Amazon QuickSight for data visualization...

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