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

Tips and tricks to optimize Amazon Athena queries

When raw data is ingested into the data lake, we can immediately create a table for that data in the AWS Glue Data Catalog (either using a Glue crawler or by running DDL statements with Athena to define the table). Once the table has been created, we can start exploring the table by using Amazon Athena to run SQL queries against the data.

However, raw data is often ingested in plaintext formats such as CSV or JSON. And while we can query the data in this format for ad hoc data exploration, if we need to run complex queries against large datasets, these raw formats are not efficient to query. There are also ways that we can optimize the SQL queries that we write to make the best use of the underlying Athena query engine, which we will review in this chapter.

By default, Amazon Athena’s cost is based on the amount of compressed data that is scanned to resolve your SQL query, so anything that can be done to reduce the...