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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 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

Challenge #2 – Total cost of ownership (TCO) is high

Engineers are tasked with making it happen! But how they make it happen is subject to many constraints. The first is cost and the second is time. Issues such as the total cost of running and managing the solution over time and the feasibility of maintaining it operationally also come into focus. The TCO for a well-engineered data solution is affected by the extract, transform, and load (ETL)/extract, load, and transform (ELT) architecture and buy versus build tooling choices for selected adopted solutions and architecture patterns. Please refer to Figure 2.3:

Figure 2.3 – TCO mind map

Figure 2.3 – TCO mind map

After reading this section, you will have a greater understanding of ETL/ELT: what it is, its origins and historical evolution, why its costs are so high, and the advantages of build versus buy. You will also learn why legacy master data management architectures are no longer in vogue.

ETL architecture...

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