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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

Niche focus areas – best practices overview

So, you will want to create a few mental bookmarks in your plan for building a future-proof data solution. The following niche data topic elaborations will provide you with new thoughts to consider as you make your design decisions.

Data profiling involves wrangling data and recording observations as metadata annotations that are associated with the data being profiled. An important attribute of that data is its context, which is defined by its semantics. Data context also has to be set in data calendars, the derived trends observed and the data’s implied source definition. Also, expectations (data contracts) add color to the data, as do the service level agreements (SLAs). There are other contexts, but these are just a few worth mentioning. The range of acceptable values that can be visualized as swimlanes will constrain what are acceptable data values for a given data context. These swimlane demarcations exist above and...

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