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

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Length 636 pages

Edition 2nd Edition

Tools

AWS

Concepts

Data Engineering

Author (1):

Gareth Eagar

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Table of Contents (24) Chapters

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

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

In this, the third and final section of the book, we examine the bigger picture of data analytics within 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 Artificial Intelligence (AI) and Machine Learning (ML) 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...

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

Table of Contents (24) Chapters

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

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Table of Contents (24) Chapters

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

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