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

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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

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Best practice organization

You will observe that we have adopted the following structure for the physical layer:

Best practice composition of the physical architecture:
- What is the structure of a physical architecture, including an example?
- What as-is/current state formalizations are required of the physical architecture?
Learn how features are operated in the context of the system’s NFRs across the data processing zones and how datasets interact with various segments of the physical architecture that is deployed with their configurations so that SLAs and the various data contracts are maintained. Each feature will have operational characteristics and process definitions aligning with the following operational responsible areas:
- ITIL Ops
- SRE
- DataOps
- DevOps
- TestOps
- MLOps
- FinOps
- SecOps
How to implement a modern, future-proof architecture:
- Configuration management (with the cloud’s structural metadata)
- Runbook
- Feature-based seleases
- Operational orchestration
- Summary service...

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

Table of Contents (21) Chapters

Best practice organization

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Authors (2)

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