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

A Deeper Dive into Data Marts and Amazon Redshift

While a data lake enables a significant amount of analytics to happen inside it, there are several use cases where a data engineer may need to load data into an external data warehouse, or data mart, to enable a set of data consumers.

As we reviewed in Chapter 2, Data Management Architectures for Analytics, a data lake is a single source of truth across multiple lines of business, while a data mart generally contains a subset of data of interest to a particular group of users. A data mart could be a relational database, a data warehouse, or a different kind of datastore.

Data marts serve two primary purposes. First, they provide a database with a subset of the data in the data lake, optimized for specific types of queries (such as for a specific business function). In addition, they also provide a higher-performing, lower-latency query engine, which is often required for specific analytic use cases (such as for powering Business...