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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

Key Considerations for IT Operational Service Best Practices

What does it mean to be operationally effective? The answer to this question is: It depends! In reality, it is based on many factors, but it all comes down to running and managing a solution during and after it is done. So, in this context, does “it” really mean “information technology” (IT) or does it mean just “it” – your system? We’ll let you decide as you read this chapter and see that without correct operational best practices IT devolves into a thing (“it”) that is not reliable, available, scalable, or performant.

With Agile and Lean development methodologies, that state of being done may never really occur. Alternatively, a product’s stage of being done may be attained only after it is ascribed a versioned release number and made generally available within one pass through the cycle. How IT gets done is iteratively defined by these cycles through the DevOps Software Development Life Cycle (SDLC).

In this chapter, we will explore the following topics:

  • Service level agreements and service level management of (various) contracts
  • Data contracts leading to quality data factory outputs
  • Solution monitoring with observability capabilities
  • System and data contract anomaly detection
  • Blue/green deployment vs other release/deploy processes on operations (what are the tradeoffs?)

With this information, you will be able to build an operational framework with processes that are sustainable when your solution enters its run/manage phase.

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