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

Summary

In this chapter, you learned more about the Amazon QuickSight service, a BI tool that is used to create and share rich visualizations of data.

We discussed the power of visually representing data, and then explored core Amazon QuickSight concepts. We looked at how various data sources can be used with QuickSight, how data can optionally be imported into the SPICE storage engine, and how you can perform some data preparation tasks using QuickSight.

We then did a deeper dive into the concepts of analyses (where new visuals are authored) and dashboards (published analyses that can be shared with data consumers). As part of this, we also examined some of the common types of visualizations available in QuickSight.

We then looked at some of the advanced features available in QuickSight. This included ML Insights (which uses ML to detect outliers in data and forecast future data trends), QuickSight Q (which enables the use of natural language queries to create visualizations...

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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

Summary

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Table of Contents (24) Chapters

Summary

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