You're reading from AWS for Solutions Architects Design your cloud infrastructure by implementing DevOps, containers, and Amazon Web Services

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781789539233

Length 454 pages

Edition 1st Edition

Tools

AWS

Concepts

Cloud Computing

Table of Contents (20) Chapters

Preface

1. Section 1: Exploring AWS

2. Chapter 1: Understanding AWS Cloud Principles and Key Characteristics FREE CHAPTER

3. Chapter 2: Leveraging the Cloud for Digital Transformation

4. Section 2: AWS Service Offerings and Use Cases

5. Chapter 3: Storage in AWS – Choosing the Right Tool for the Job

6. Chapter 4: Harnessing the Power of Cloud Computing

7. Chapter 5: Selecting the Right Database Service

8. Chapter 6: Amazon Athena – Combining the Simplicity of Files with the Power of SQL

9. Chapter 7: AWS Glue – Extracting, Transforming, and Loading Data the Simple Way

10. Chapter 8: Best Practices for Application Security, Identity, and Compliance

11. Section 3: Applying Architectural Patterns and Reference Architectures

12. Chapter 9: Serverless and Container Patterns

13. Chapter 10: Microservice and Event-Driven Architectures

14. Chapter 11: Domain-Driven Design

15. Chapter 12: Data Lake Patterns – Integrating Your Data across the Enterprise

16. Chapter 13: Availability, Reliability, and Scalability Patterns

17. Section 4: Hands-On Labs

18. Chapter 14: Hands-On Lab and Use Case

19. Other Books You May Enjoy

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Understanding how Amazon Athena works

Amazon Athena originally was intended to work with data stored in Amazon S3. As we will see in a later section in this chapter, it can now work with other source types as well.

This feature of Amazon Athena is a game-changer. You can now combine disparate data sources just as easily as if they all had the same format. This enables you to join a JSON file with a CSV file or a DynamoDB table with an Amazon Redshift table.

Previously, if you wanted to combine this data, it would require performing this combination programmatically, which would invariably translate into a long development cycle and more than likely not scale well when using large datasets.

Now all you have to do is write a SQL query that combines the two data sources. Due to the underlying technology used, this technique will scale well, even when querying terabytes' and petabytes' worth of data.

Data scientists and data analysts will be able to work at a speed...