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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Summary

One needs to grasp the concept of the data mesh and its core principles but apply practical data fabric DataOps features to make the data mesh implementation work as expected.

Data being a product makes sense, but it needs to be fabricated, produced, and curated to the point that data-derived information is ready for self-service, which can be leveraged for knowledge pop-out insights, leading to wise decisions. This progression in the past was forced through big systems software development, but today and going forward, it will involve automated intelligence brought about by the data engineer.

Data needs to stand alone and be smart as well as enabled for insight harvesting. Data needs to be smarter than it is today! The wording of that implies a personification of data. This is a data engineer’s goal. Engineered data is no longer hindered by today’s big data approaches. Data that is of the highest quality, fully understood, linkable, transparently curated...