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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

Common big data design patterns

Design patterns provide a common vocabulary for data personas to understand and share design and architecture blueprints. So, given a set of requirements, everyone understands the likely design pattern to apply as a plausible solution. Traditional software engineering design patterns are object-oriented and are of three types, categorized under creational, structural, and behavioral patterns. Data engineering is not necessarily object-oriented (OO) related and is better articulated around concepts of data ingestion, transformation, storage, and analytics patterns. In the next few sections, we will look at reusable patterns in each of these areas.

Ingestion

Ingestion refers to all aspects of consolidating data into a target site for further processing and analysis from multiple sources using different languages, different file formats, and different sizes and frequencies. The number of such combinations is large, which is why we see a lot of data...

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