You're reading from Data Engineering Best Practices Architect robust and cost-effective data solutions in the cloud era

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781803244983

Length 550 pages

Edition 1st Edition

Languages

SQL

Tools

Google cloud SQL

Concepts

Data Engineering

Authors (2):

David Larochelle

Richard J. Schiller

View More author details

Table of Contents (21) Chapters

Preface

1. Chapter 1: Overview of the Business Problem Statement FREE CHAPTER

2. Chapter 2: A Data Engineer’s Journey – Background Challenges

3. Chapter 3: A Data Engineer’s Journey – IT’s Vision and Mission

4. Chapter 4: Architecture Principles

5. Chapter 5: Architecture Framework – Conceptual Architecture Best Practices

6. Chapter 6: Architecture Framework – Logical Architecture Best Practices

7. Chapter 7: Architecture Framework – Physical Architecture Best Practices

8. Chapter 8: Software Engineering Best Practice Considerations

9. Chapter 9: Key Considerations for Agile SDLC Best Practices

10. Chapter 10: Key Considerations for Quality Testing Best Practices

11. Chapter 11: Key Considerations for IT Operational Service Best Practices

12. Chapter 12: Key Considerations for Data Service Best Practices

13. Chapter 13: Key Considerations for Management Best Practices

14. Chapter 14: Key Considerations for Data Delivery Best Practices

15. Chapter 15: Other Considerations – Measures, Calculations, Restatements, and Data Science Best Practices

16. Chapter 16: Machine Learning Pipeline Best Practices and Processes

17. Chapter 17: Takeaway Summary – Putting It All Together

18. Chapter 18: Appendix and Use Cases

19. Index

Why subscribe?

20. Other Books You May Enjoy

SBP 9 – define and implement NFRs first

NFRs delineate how a system operates, rather than the specific functionalities it performs.

In essence, while functional requirements (FRs) might dictate what a system does, NFRs specify how well it does those tasks. Common examples include performance benchmarks, scalability thresholds, system reliability, security standards, and maintainability guidelines. You will learn more about why NFRs need to be built first and then the functional requirements (FRs) as part of the effort to implement your OKRs.

Distinguishing functional (FRs) from non-functional requirements (NFRs)

FRs are explicit about the functions a system must perform. For instance, a data analytics application might need to generate specific reports from input data (an FR). On the other hand, NFRs might dictate that these reports be generated within a specific time frame, that the system can handle a certain volume of data inputs without degradation in performance...