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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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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

Prevent Agile from being fragile

Agile can be fragile! You must first develop an organizational Agile Manifesto that defines a workable Agile methodology for the organization. This is necessary to discourage teams from adopting any flavor of the day Agile approach that some genius decides is going to be good for everyone! Mitigating the chaos that misaligned Agile frameworks produce, such as scrum or Kanban, is essential. Disagreements in methodology can transform a healthy team’s workflow into confusion and result in ultra poor performance. There are many flavors of Agile! See 7 Types Of Agile Methodologies {https://packt-debp.link/7fixFt} and 12 Agile methodologies: Pros, cons, and when to use them {https://packt-debp.link/3mXhBA}. Scrum and Kanban are two of the most prominent Agile methodologies, each with its own principles and methodologies. Scrum, known for its structured approach and time-boxed sprints, is often favored in projects requiring clear milestones and deliverables...

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