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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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Toc

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

In this chapter, we talked about the importance of data modeling exercises to organize and persist the data while designing a new ETL use case so that subsequent data operations can benefit from an optimal balance of performance, cost, efficiency, and quality.

A good data model helps us with faster query speeds and reduces unnecessary I/O throughput brought about by expensive wasted scans. A design-first approach forces us to think through the data relations and can not only help reduce data redundancy but also help improve the reuse of pre-computed results, thereby reducing storage and computing costs for big data platforms. The increase in efficiency of data utilization improves the overall user experience. Having stable base datasets ensures more consistency of derived datasets further down the pipeline, thereby improving the quality of generated insights.

In the next chapter, we will look at the Delta protocol and the main features that help bring reliability, perfor...

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