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

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While there are many good ways to use a data warehouse for analytics, there are some things that organizations may be tempted to do that are not good for a data warehouse.

Let's take a look at some of the ways of using a data warehouse that should be avoided.

Using a data warehouse as a transactional datastore

Data warehouses are designed to be optimized for online analytical processing (OLAP) queries, so they should not be used for online transaction processing (OLTP) queries and use cases.

While there are mechanisms to update or delete data from a data warehouse, a data warehouse is primarily designed for append-only queries. There are also other features of transactional databases (such as MySQL or PostgreSQL) that are available in Redshift – such as the concept of primary and foreign keys – but these are used for performance optimization and query planning and are not enforced by Redshift...