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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

SBP 6 – implement the difficult first!

In software engineering, there’s a prevailing adage:

“Eat the frog!”

“Eat That Frog!: 21 Great Ways to Stop Procrastinating and Get More Done in Less Time” (https://www.amazon.com/Eat-That-Frog-Great-Procrastinating/dp/162656941X).

This metaphorical advice suggests tackling the most challenging task of your day first, freeing you from the looming dread of facing it later. Similarly, in data engineering, we adapt this notion to our processes, emphasizing the importance of confronting the most complex problems at the outset. It’s not just about getting the hard tasks out of the way; it’s a strategic maneuver with profound implications. Why you should tackle difficult issues first will be explained in the following sections.

The philosophy of tackling the hard tasks first

Tackling the hardest problems first may appear counterintuitive, especially in a discipline as nuanced as...

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