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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 9: Delta for Reproducible Machine Learning Pipelines

"Repetition is the mother of learning, the father of action, which makes it the architect of accomplishment."

– Zig Ziglar, American author and motivational speaker

In previous chapters, we established the pivotal nature of Delta in architecting data pipelines. What about Machine Learning (ML) pipelines? They involve different personas with different skills and needs. ML has been around for a while; what has changed lately is broad access to large datasets and affordable compute, which has now made it possible for everyone to tinker with ML. Can Delta stand the litmus test of building a reproducible ML pipeline just as effectively as a data pipeline? There are specific challenges and nuances in building a model, staging it in production, and repeating the process over and over again. In this chapter, we will look into these challenges and map the capabilities of Delta...

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