You're reading from Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

Product type Paperback

Published in Dec 2021

Publisher Packt

ISBN-13 9781800560413

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Analysis

Author (1):

Gareth Eagar

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. Chapter 1: An Introduction to Data Engineering FREE CHAPTER

3. Chapter 2: Data Management Architectures for Analytics

4. Chapter 3: The AWS Data Engineer's Toolkit

5. Chapter 4: Data Cataloging, Security, and Governance

6. Section 2: Architecting and Implementing Data Lakes and Data Lake Houses

7. Chapter 5: Architecting Data Engineering Pipelines

8. Chapter 6: Ingesting Batch and Streaming Data

9. Chapter 7: Transforming Data to Optimize for Analytics

10. Chapter 8: Identifying and Enabling Data Consumers

11. Chapter 9: Loading Data into a Data Mart

12. Chapter 10: Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Chapter 11: Ad Hoc Queries with Amazon Athena

15. Chapter 12: Visualizing Data with Amazon QuickSight

16. Chapter 13: Enabling Artificial Intelligence and Machine Learning

17. Chapter 14: Wrapping Up the First Part of Your Learning Journey

18. Other Books You May Enjoy

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 load of data is completed to the data lake, 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 a new insert to the table.
U – Update: This indicates that...