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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

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781803244983

Length 550 pages

Edition 1st Edition

Languages

SQL

Tools

Google cloud SQL

Concepts

Data Engineering

Authors (2):

David Larochelle

Richard J. Schiller

View More author details

Table of Contents (21) Chapters

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

Why subscribe?

20. Other Books You May Enjoy

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Authors (2)

Richard J. Schiller

Richard J. Schiller

Richard J. Schiller is a chief architect, distinguished engineer, and startup entrepreneur with 40 years of experience delivering real-time large-scale data processing systems. He holds an MS in computer engineering from Columbia University's School of Engineering and Applied Science and a BA in computer science and applied mathematics. He has been involved with two prior successful startups and has co authored three patents. He is a hands-on systems developer and innovator.

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David Larochelle

David Larochelle

David Larochelle has been involved in data engineering for startups, Fortune 500 companies, and research institutes. He holds a BS in computer science from the College of William & Mary, a Masters in computer science from the University of Virginia, and a Master's in communication from the University of Pennsylvania. David's career spans over 20 years, and his strong background has enabled him to work in a wide range of organizations, including startups, established companies, and research labs.

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