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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

Data mashups using Delta

Data mashup refers to combining different datasets to provide a unified data view for analytics. A simple example of this is a BI dashboard combining a consumer's interaction with a brand. The browsing and purchase history are transactional structured data; log data is semi-structured data; the tweets, support cases around product inquiry or complaint; social media posts and comments including text and images are examples of unstructured data that can provide insights into the voice of the customer and user sentiment.

The important elements can be extracted, aggregated, predicted, and brought together with actual transactional data to predict the consumer's next move. The ability to use SQL to query complex aspects of unstructured data and join it with a primary key, such as the customer ID, is very powerful and empowers self-service capabilities. Marketing dollars spent on personalized advertisements and product recommendations can then be purposefully...

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