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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

Summary

Delta Lake with ACID transactions makes it much easier to reliably perform UPDATE and DELETE operations. Delta introduces the MERGE INTO operator to perform Upsert/Merge actions as atomic operations along with time travel features to provide rewind capabilities on Delta Lake tables. Cloning, CDC, and SCD are patterns found in several use cases that build upon these base operations. In this chapter, we have looked at these common data patterns and shown how Delta continues to provide efficient, robust, and elegant solutions to simplify the everyday work scenarios of a data persona, allowing them to focus on the use case at hand.

In the next chapter, we will look at data warehouse use cases and see if all of them can be accommodated in the context of a data lake. We will reflect on whether there is a better architecture strategy to consider instead of just shunting between warehouses and lakes.

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