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Data Engineering with AWS - Second Edition

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
Author (1):
Gareth Eagar Gareth Eagar
Profile icon Gareth Eagar
Toc

Table of Contents (24) Chapters close

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 sample architecture for a data mesh on AWS

We have looked at what a data mesh is, some of the organizational and technical challenges of building a data mesh, and finally some of the AWS services that can be used to build a data mesh. Now, in this section, let’s bring it all together with a sample architecture for a data mesh on AWS.

Architecture for a data mesh using AWS-native services

Earlier in this chapter, we discussed how a data mesh is easier to build if starting new, or if just using AWS-native services. In this section, we will look at a sample architecture for when you only use AWS-native services, and in the section after this, we will review an architecture for environments that use analytic tools from other vendors.

The following architecture diagram shows a data mesh that is built using Amazon DataZone, with data in an Amazon S3-based data lake (a similar architecture would support data in Amazon Redshift) and using AWS Lake Formation for data sharing...

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