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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

Summary

In this chapter, we explained the challenges data engineers will face when crafting a future-proof data engineered solution. Some core challenges have been outlined that will be faced when managing vast data, evolving technologies, and ensuring efficient data pipelines:

  • Platform architectures change rapidly based on the cloud provider’s user demands, combined with shifting technology opportunities
  • The total cost of your ownership (TCO) is high if you do not build a future-proof solution
  • The data and system architecture that your design conforms to must be rational and handle important items first and not have them appear as obstacles later so that your future-proof goals are attainable

Data engineering remains a hard task, but you are going into the effort with your eyes open. A solid foundation has been laid for the principles to be discussed later. We provided an overview of data engineering approaches so that you can scope out the current and...

You have been reading a chapter from
Data Engineering Best Practices
Published in: Oct 2024
Publisher: Packt
ISBN-13: 9781803244983
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