You're reading from Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

Product type Paperback

Published in Dec 2021

Publisher Packt

ISBN-13 9781800560413

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Analysis

Author (1):

Gareth Eagar

View More author details

Table of Contents (19) Chapters

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

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.

Amazon Athena's cost is based on the amount of compressed data that is scanned to resolve the query, so anything that can be done to reduce the amount of data scanned improves query performance and reduces...