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Simplifying Data Engineering and Analytics with Delta

You're reading from   Simplifying Data Engineering and Analytics with Delta Create analytics-ready data that fuels artificial intelligence and business intelligence

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781801814867
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Anindita Mahapatra Anindita Mahapatra
Author Profile Icon Anindita Mahapatra
Anindita Mahapatra
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Table of Contents (18) Chapters Close

Preface 1. Section 1 – Introduction to Delta Lake and Data Engineering Principles
2. Chapter 1: Introduction to Data Engineering FREE CHAPTER 3. Chapter 2: Data Modeling and ETL 4. Chapter 3: Delta – The Foundation Block for Big Data 5. Section 2 – End-to-End Process of Building Delta Pipelines
6. Chapter 4: Unifying Batch and Streaming with Delta 7. Chapter 5: Data Consolidation in Delta Lake 8. Chapter 6: Solving Common Data Pattern Scenarios with Delta 9. Chapter 7: Delta for Data Warehouse Use Cases 10. Chapter 8: Handling Atypical Data Scenarios with Delta 11. Chapter 9: Delta for Reproducible Machine Learning Pipelines 12. Chapter 10: Delta for Data Products and Services 13. Section 3 – Operationalizing and Productionalizing Delta Pipelines
14. Chapter 11: Operationalizing Data and ML Pipelines 15. Chapter 12: Optimizing Cost and Performance with Delta 16. Chapter 13: Managing Your Data Journey 17. Other Books You May Enjoy

Handling CDC

CDC is a process that identifies the classification of incoming records in real-time to determine which ones are brand new, which ones are modifications of existing data, and which ones are requests for deletes. Operational data stores are capturing transactions in OLTP systems continuously and streaming them across to OLAP systems. These two data systems need to be kept in sync to reconcile data and keep data fidelity. It is like a replay of the operations but on a different system.

CDC

This is the flow of data from the OLTP system into an OLAP system, typically the first landing zone, which is referred to as the bronze layer in the medallion architecture. Several tools, such as GoldenGate from Oracle or PowerGate from Informatica, support the generation of change sets, or they could be generated by other relational stores that capture this information on a data modification trigger. Moreover, this could be an omni-channel scenario where the same type of data is...

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