You're reading from Data Engineering with AWS - Second Edition

Product type Book

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Pages 636 pages

Edition 2nd Edition

Languages

Concepts

Data Engineering

Author (1):

Gareth Eagar

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering

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

Redshift architecture review and storage deep dive

In this section, we will take a deeper dive into the architecture of Redshift clusters, as well as into how data in tables is stored across Redshift nodes. This in-depth look will help you understand and fine-tune Redshift’s performance, though we will also cover how many of the design decisions affecting table layout can be automated by Redshift.

In Chapter 2, Data Management Architectures for Analytics, we briefly discussed how the Redshift architecture uses leader and compute nodes. Each compute node contains a certain amount of compute power (CPUs and memory), as well as a certain amount of local storage. When configuring your Redshift cluster, you can add multiple compute nodes, depending on your compute and storage requirements. Note that to provide fault tolerance and improved durability, the compute nodes have 2.5–3x the stated node storage capacity (for example, if the addressable storage capacity is listed...