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

Exploring advanced Redshift features

While Redshift was first launched a long while back (2013), AWS is continually adding new functionality and features to Redshift. In this section, we are going to look at some of the advanced capabilities launched in the past few years that can help you get the most from your Redshift cluster. You should also regularly review the AWS What’s New page for Redshift (https://aws.amazon.com/redshift/whats-new/), as well as AWS blog posts tagged with Redshift (https://aws.amazon.com/blogs/big-data/tag/amazon-redshift/), to ensure you keep up to date with new features.

Data sharing between Redshift clusters

There are a number of use cases where you may want to share data from one Redshift cluster with data in another (or multiple other) Redshift cluster. For example, if you implement a data mesh architecture, you may want to make data available from one part of the business easily accessible for other parts of the business without needing...