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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

Chapter 3: Delta – The Foundation Block for Big Data

"Without a solid foundation, you will have trouble creating anything of value."

– Erica Oppenheimer, on academic mastery

In the previous chapters, we looked at the trends in big data processing and how to model data. In this chapter, we will look at the need to break down data silos and consolidate all types of data in a centralized data lake to get holistic insights. First, we will understand the importance of the Delta protocol and the specific problems that it helps address. Data products have certain repeatable patterns and we will apply Delta in each situation to analyze the before and after scenarios. Then, we will look at the underlying file format and the components that are used to build Delta, its genesis, and the high-level features that make Delta the go-to file format for all types of big data workloads. It makes not only the data engineer's job easier, but also other data personas...

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