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Essential PySpark for Scalable Data Analytics

You're reading from   Essential PySpark for Scalable Data Analytics A beginner's guide to harnessing the power and ease of PySpark 3

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Length 322 pages
Edition 1st Edition
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Author (1):
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Sreeram Nudurupati Sreeram Nudurupati
Author Profile Icon Sreeram Nudurupati
Sreeram Nudurupati
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer FREE CHAPTER 3. Chapter 2: Data Ingestion 4. Chapter 3: Data Cleansing and Integration 5. Chapter 4: Real-Time Data Analytics 6. Section 2: Data Science
7. Chapter 5: Scalable Machine Learning with PySpark 8. Chapter 6: Feature Engineering – Extraction, Transformation, and Selection 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Machine Learning Life Cycle Management 12. Chapter 10: Scaling Out Single-Node Machine Learning Using PySpark 13. Section 3: Data Analysis
14. Chapter 11: Data Visualization with PySpark 15. Chapter 12: Spark SQL Primer 16. Chapter 13: Integrating External Tools with Spark SQL 17. Chapter 14: The Data Lakehouse 18. Other Books You May Enjoy

Change Data Capture

Generally, operational systems do not maintain historical data for extended periods of time. Therefore, it is essential that an exact replica of the transactional system data be maintained in the data lake along with its history. This has a few advantages, including providing you with a historical audit log of all your transactional data. Additionally, this huge wealth of data can help you to unlock novel business use cases and data patterns that could take your business to the next level.

Maintaining an exact replica of a transactional system in the data lake means capturing all of the changes to every transaction that takes place in the source system and replicating it in the data lake. This process is generally called CDC. CDC requires you to not only capture all the new transactions and append them to the data lake but also capture any deletes or updates to the transactions that happen in the source system. This is not an ordinary feat to achieve on data...

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