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Data Engineering Best Practices

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

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781803244983
Length 550 pages
Edition 1st Edition
Languages
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Authors (2):
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David Larochelle David Larochelle
Author Profile Icon David Larochelle
David Larochelle
Richard J. Schiller Richard J. Schiller
Author Profile Icon Richard J. Schiller
Richard J. Schiller
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Toc

Table of Contents (21) Chapters Close

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 20. Other Books You May Enjoy

Summary

In this chapter, you were exposed to a technology deep dive with lots of references to research and come up to speed on the topics of any blind spots. Then, we dove into three high level use cases. Although they may have lacked serious details, they have been portrayed descriptively enough for you to get the general idea. The challenge has always been to leverage your data in new ways and to organize it in a way that makes that possible. Data needs to be smart, fungible, and linkable.

Over time, the future will show us how trust can be enforced among the roles both within and external to your IT organization. Data silos will come down when the truth of the underliyng data is transparently preserved with formal semantics. Data consumers will know that the information presented is correct, complete, governed, and able to be used as intended. This makes the promise of an AI ecosystem a reality, because the errors are reduced during RAG processing, prompt engineering/tuning...

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