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

Metadata strategy

What’s metadata? The setup for this answer was provided in Chapter 3. Much emphasis was placed on metadata’s three types: descriptive metadata (to catalog dataset contents), administrative or lineage metadata (the manner in which the dataset was curated as well as cloud topology and configurations), and structural or semantic metadata (formal information as semantic knowledge). Also, in Chapter 4, a number of principles were laid out to bring the issue into focus. The need for metadata in your solution is aligned with these principles:

  • Principle #10: Metadata is associated with datasets and is relevant to the business
  • Principle #11: Dataset lineage and at-rest metadata is subject to life cycle governance
  • Principle #12: Datasets and metadata require cataloging and discovery services
  • Principle #13: Semantic metadata guarantees correct business understanding at all stages in the data journey
  • Principle #15: Implement foundational...
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