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Data Engineering with AWS

You're reading from   Data Engineering with AWS Acquire the skills to design and build AWS-based data transformation pipelines like a pro

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Length 636 pages
Edition 2nd Edition
Tools
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Author (1):
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Gareth Eagar Gareth Eagar
Author Profile Icon Gareth Eagar
Gareth Eagar
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Table of Contents (24) Chapters Close

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):

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