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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

Trade-offs in designing streaming architectures

Spark has multiple ways of achieving the end goal with tunable performance, cost, and quality. Hence, there is a need for a process to understand the goals/requirements of use cases. For each goal, define the strategy needed; for each goal, define the resources required; compute the cost of resources employed. The process is repeated until expectations are balanced. This is where trade-offs need to be considered, as shown in the following diagram; either the goal or resource has to be tweaked:

Figure 4.14 – Balancing streaming service requirements and resources

It is a balancing act of managing the various goals with the resources that the team is willing to bring to the table. Goals refer to requirements regarding scalability, performance, processing logic, quality, reliability, and availability. Resources refer to compute, storage, and integration services, and the effort needed to not only create but...

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