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

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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

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Consumable data delivery as a repository

With many legacy data engineering approaches, the output was just a report, then over the years the report needed to be used to drive decisions, and as a result, decision support systems (DSS) were developed with MOLAP (multidimensional online analytical processing), OLAP, and ROLAP (relational online analytical processing) data mart technologies in the backend. The days of PowerBuilder and Crystal Reports became the days of Cognos, Hyperion, Micro Strategies, SAS, Power BI, and many others. Even these Data Mart-centered architectures are considered legacy approaches today.

In the future, you will want to focus on the creation of a secure data mesh or data fabric as the delivery target for curated data. With these patterns, we overcome the data volume issues present in legacy DSS systems without sacrificing observable analytic query performance since exabytes of data just do not load into tools such as Power BI. This is an evident truth because...

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You're reading from Data Engineering Best Practices Architect robust and cost-effective data solutions in the cloud era

Table of Contents (21) Chapters

Consumable data delivery as a repository

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Authors (2)

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