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

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 addressable storage capacity is listed as 2.56 TB...