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

Working with Change Data Capture (CDC) data

One of the most challenging aspects of working within a data lake environment is the processing of updates to existing data, such as with Change Data Capture (CDC) data. We have discussed CDC data previously, but as a reminder, this is data that contains updates to an existing dataset.

A good example of this is data that comes from a relational database system. After the initial loading of data to the data lake is complete, a system (such as Amazon DMS) can read the database transaction logs and write all future database updates to Amazon S3. For each row written to Amazon S3, the first column of the CDC file would contain one of the following characters (see the section on Amazon DMS in Chapter 3, The AWS Data Engineer’s Toolkit, for an example of a CDC file generated by Amazon DMS):

I – Insert: This indicates that this row contains data that was newly inserted into the table
U – Update: This indicates...