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Simplifying Data Engineering and Analytics with Delta

You're reading from  Simplifying Data Engineering and Analytics with Delta

Product type Book
Published in Jul 2022
Publisher Packt
ISBN-13 9781801814867
Pages 334 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Anindita Mahapatra Anindita Mahapatra
Profile icon Anindita Mahapatra

Table of Contents (18) Chapters

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