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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

It is interesting to note how the term data lake came about. It is not called a pond as a pond is perceived to be small. It is not called a sea or ocean because the saltwater makes it look murky and the waves are rough and uncontrolled. It is not called a stream as "streaming" is already heavily used in the context of real-time processing. It is not a river because water drains off, whereas the vision of a data lake is that of a pristine reservoir of water that provides food and shelter to a lot of flora and fauna and could turn into a swamp if you're not careful with governance and management. In this chapter, we went over the need for data consolidation and how Delta helps with data reliability, quality, and governance, giving us curated analytics-ready data and preventing silos and swamps. Data, once curated, remains in an open format and is used in multiple use cases by different data personas, enabling them to be more agile in on-boarding new use cases and...

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