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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

Section 2 – End-to-End Process of Building Delta Pipelines

In this section, you will learn how Delta aids the plumbing and heavy lifting of raw data to refine it for analytic purposes. Instead of spending time, effort, and resources on additional code to address everyday data challenges, data staff can now focus primarily on the business use case. Delta provides constructs for greater reliability, quality, and performance of your data and is becoming the de-facto standard for big data projects as it greatly simplifies the tasks of all the data staff involved in creating a data product or service.

This part includes the following chapters:

  • 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...
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