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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

Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

In Section 3 of the book, we examine the bigger picture of data analytics in modern organizations. We learn about the tools that data consumers commonly use to work with data transformed by data engineers, and briefly look into how machine learning (ML) and artificial intelligence (AI) can draw rich insights out of data. We also get hands-on with tools for running ad hoc SQL queries on data in the data lake (Amazon Athena), for creating data visualizations (Amazon QuickSight), and for using AI to derive insights from data (Amazon Comprehend). We then conclude by looking at data engineering examples from the real world and explore some emerging trends in data engineering.

This section comprises the following chapters:

  • Chapter 11, Ad Hoc Queries with Amazon Athena
  • Chapter 12, Visualizing Data with Amazon QuickSight
  • Chapter 13, Enabling Artificial Intelligence and Machine Learning...
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