You're reading from Data Engineering with AWS Acquire the skills to design and build AWS-based data transformation pipelines like a pro

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Length 636 pages

Edition 2nd Edition

Tools

AWS

Concepts

Data Engineering

Author (1):

Gareth Eagar

View More author details

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering FREE CHAPTER

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

What not to do – anti-patterns for a data warehouse

While there are many good ways to use a data warehouse for analytics, there are some approaches that at first may seem to be a good fit for a data warehouse but are generally not recommended.

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 (such as the merge statement in Redshift), a data warehouse is primarily designed for mostly append-only, or insert, 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...