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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

From business problem to insight generation

No data science or ML is justified purely by tech; there needs to be a business problem at hand. The business team usually comes up with a prioritized list of problems to solve. The consumers could be an internal or external audience. Dashboards are a great way to communicate insights. The key metrics are constantly tracked and transparently available for everyone in the value chain.

Let us look at the four cooperating pipelines discussed in this chapter through the lens of ownership and handoffs. The skills, tools, and frameworks that these personas wield are sometimes non-overlapping, so it is interesting to consider how they collaborate with one another to create the final product or service and satisfy their stakeholders in the business.

It is fair to say that the data engineer is responsible for the Extract Transform Load (ETL) process and bringing in different data sources. However, they may not have much domain knowledge, so...

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