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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

Scaling and high availability

Scalability refers to the elasticity of compute resources, meaning adding more compute capacity as data volume increases to support a heavier workload. It is sometimes necessary to scale down resources that aren't in use to save compute costs. Scaling can be of two types: vertical or horizontal. Vertical scaling refers to replacing existing node types with bigger instance types. This is not sustainable after a point because there is an upper bound on the largest possible instance. Horizontal scaling refers to the addition of more worker nodes of the same type and is truly infinitely scalable. Each serves different scenarios. If the largest partition is no longer divisible, we benefit from a bigger node type. However, the advantage is that some of the nodes can be turned off when there is low data volume. This is an infrastructure and architecture capability and not directly related to Delta.

High availability (HA) refers to the system uptime...

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