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

View More author details

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, we learned more about the Amazon Athena service, an AWS-managed service that builds on the Apache Presto and Trino solutions to enable you to run SQL or Spark based queries against your data. We also looked at how to optimize our data and SQL queries to increase query performance and reduce costs.

Then, we explored advanced Athena functionality, including how Athena can be used as a SQL query engine not only for data in an Amazon S3 data lake, but also for external data sources such as other database systems, data warehouses, and even CloudWatch logs, using Athena Query Federation.

We wrapped up the theory part of this chapter by looking at Athena workgroups, which let us manage governance and costs, and they can be used to enforce specific settings for different teams or projects, and can also be used to limit the amount of data that is scanned by queries. In the last section of this chapter, we got hands-on with Athena, first creating a new workgroup...