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

Loading data into data marts

Many tools can work directly with data in the data lake, as we covered in Chapter 3, The AWS Data Engineer’s Toolkit. These include tools for ad hoc SQL queries (Amazon Athena), data processing tools (such as Amazon EMR and AWS Glue), and even specialized machine learning tools (such as Amazon SageMaker).

These tools read data directly from Amazon S3, but there are times when a use case may require much lower latency and higher performance reads of the data. Alternatively, there may be times when the use of highly structured schemas may best meet the analytic requirements of the use case. In these cases, loading data from the data lake into a data mart makes sense.

In analytic environments, a data mart is most often a data warehouse system (such as Amazon Redshift or Snowflake), but it could also be a relational database system (such as Amazon RDS for MySQL), depending on the use case’s requirements. In either case, the system will...