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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

Summary

In this chapter, you were exposed to a number of areas for consideration in your designs. For data profiling, you want to really know your data, find data areas that are anomalies, and be ready to smooth the data, fill gaps, and if necessary, create temporary adjustments with synthetic data until real data can be supplied. When implementing a data factory, you need to know that the data is of the highest quality and that includes the core data, its metadata, and its trends. You should also include its data profile in that list.

You also learned that raw data is messy and has legitimate gaps and illegitimate (erroneous) gaps that need correction. It is important that you also know the shape of the data as defined by its profile so that it can be semantically maintained in downstream processing and not cause data to be misused.

You also learned about data calendars and that data has to be interpreted in context, one facet of which is the data calendar. This is essential...

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