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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

Chapter 11: Operationalizing Data and ML Pipelines

"We are what we repeatedly do. Excellence, then, is not an act, but a habit."       

–  Aristotle

In the previous chapters, we saw how Delta helps to democratize data products and services and facilitates data sharing within the organization and externally with vendors and partners. Creating a Proof of Concept (POC) happy path to prove what is feasible is a far cry from taking a workload to production. Stakeholders and consumers get upset when their reports are not available in a timely manner and, over time, lose confidence in the team's ability to deliver on their promises. This affects the profitability metrics of the business.

In this chapter, we will look into the aspects of DevOps that harden a pipeline so that it stands the test of time and people do not...

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