You're reading from AWS for Solutions Architects The definitive guide to AWS Solutions Architecture for migrating to, building, scaling, and succeeding in the cloud

Product type Paperback

Published in Apr 2023

Publisher Packt

ISBN-13 9781803238951

Length 692 pages

Edition 2nd Edition

Tools

AWS

Concepts

Cloud Computing

Authors (4):

Neelanjali Srivastav

Saurabh Shrivastava

Alberto Artasanchez

Imtiaz Sayed

View More author details

Table of Contents (19) Chapters

AWS for Solutions Architects, Second Edition: Design your cloud infrastructure by implementing DevOps, containers, and Amazon Web Services

1 Understanding AWS Principles and Key Characteristics FREE CHAPTER

2 Understanding AWS Well-Architected Framework and Getting Certified

3 Leveraging the Cloud for Digital Transformation

4 Networking in AWS

5 Storage in AWS – Choosing the Right Tool for the Job

6 Harnessing the Power of Cloud Computing

7 Selecting the Right Database Service

8 Best Practices for Application Security, Identity, and Compliance

9 Dive efficiency with Cloud Operation Automation and DevOps in AWS

10 Bigdata and streaming data processing in AWS

11 Datawarehouse, Data Query and Visualization in AWS

12 Machine Learning, IoT, and Blockchain in AWS

13 Containers in AWS

14 Microservice and Event-Driven Architectures

15 Domain-Driven Design

16 Data Lake Patterns – Integrating Your Data across the Enterprise

17 Availability, Reliability, and Scalability Patterns

18 AWS Hands-On Lab and Use Case

Understanding how Amazon Athena works

Amazon Athena initially 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 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, performing a combination programmatically 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, this technique will scale well, even when querying terabytes and petabytes of data.

Data scientists and data analysts will be able to work at a speed that would have been impossible just a few years ago.

Under the hood, Amazon...