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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

DaaS

Every company wants to be data-driven but just collecting data doesn't make you a data-driven enterprise. However, having actionable customer-centric insights does. If there is a data provider that can do the heavy lifting for easy consumption, then every business can use its services to be more competitive and truly data-driven. Being a DaaS provider is hard because taming raw data is a herculean task and it takes a lot of planning and execution to pull it off. The typical activities to manage data include the following:

  • Data collection to ensure quality and timely data
  • Data aggregation to summarize data in well-known dimensions for actionable insights and to avoid analysis paralysis
  • Data correlation for proper data and risk modeling to use the predictive value inherent in the datasets
  • Qualitative analysis to ensure that there is statistical significance and insights generated from the data that can be relied upon
  • Advanced BI and AI analytics to...
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