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 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

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SBP 12 – choose languages with solid reasoning

Diversity in language choice brings forth a richness of capabilities but also comes with the inherent challenge of managing complexity. It’s a delicate balance between leveraging the strengths of various languages and not overwhelming the engineering process.

Key languages in data engineering and their roles

Within data engineering, specific languages have established their significance:

Python (3.10.x specifically): Dominates modern data processing due to its vast ecosystem. Its simplicity and the powerful libraries available for data analysis, machine learning, and automation are unparalleled.
SQL: Remains indispensable for data querying and management, transcending the test of time.
Java: Known for its robustness and portability, Java plays a significant role in large-scale data processing systems and big data ecosystems. It’s particularly valued for its performance in distributed computing...