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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 summary, this chapter brought you on a journey where you were able to discover what goes into conceptual architecture and conceptual reference architecture. You also discovered key aspects of the architecture driving the best practices that will be evident in the book. By forming the conceptual reference architecture diagram, you created an architecture that could be communicated. Via a due diligence process, you made important objectively obtained vendor and tool decisions, and you learned that by itemizing capabilities needed in the solution, you justified the drivers for the logical architecture.

Additionally, new terminology was explained and a glossary was produced for clarity. Data class, data movement, and data formats in zones were identified. ELT is better than ETL (refer back to Chapter 2, where this was discussed), so we can get back to the raw data of the system that is being transformed multiple times in order to get it curated into final form. The data factory...

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