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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 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

Test framework example

Now that you have become aware of some of the approaches to testing, it’s time to look at a practical example and for that, we have to begin with key drivers. Please look at Figure 10.1, where you can see a test framework that you can take into account as you develop your solution. It should be formalized and made available to all those on the solution’s various project teams to utilize. In Figure 10.1 you can see that your test framework, integration, and best practices work together to form the scope of your test effort:

Figure 10.1 – Azure DevOps scope

Figure 10.1 – Azure DevOps scope

Remember that the goal is to reduce the overhead of testing and make it as easy as possible to run tests. The developers and testers need to be able to catch deviations from the status quo as development sprints release code into various developed environments. You will know that the system is minimally up and running based on integration tests that are running...

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