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Fundamentals of Analytics Engineering

You're reading from   Fundamentals of Analytics Engineering An introduction to building end-to-end analytics solutions

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781837636457
Length 332 pages
Edition 1st Edition
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Authors (7):
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Dumky De Wilde Dumky De Wilde
Author Profile Icon Dumky De Wilde
Dumky De Wilde
Ricardo Angel Granados Lopez Ricardo Angel Granados Lopez
Author Profile Icon Ricardo Angel Granados Lopez
Ricardo Angel Granados Lopez
Lasse Benninga Lasse Benninga
Author Profile Icon Lasse Benninga
Lasse Benninga
Taís Laurindo Pereira Taís Laurindo Pereira
Author Profile Icon Taís Laurindo Pereira
Taís Laurindo Pereira
Jovan Gligorevic Jovan Gligorevic
Author Profile Icon Jovan Gligorevic
Jovan Gligorevic
Juan Manuel Perafan Juan Manuel Perafan
Author Profile Icon Juan Manuel Perafan
Juan Manuel Perafan
Fanny Kassapian Fanny Kassapian
Author Profile Icon Fanny Kassapian
Fanny Kassapian
+3 more Show less
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Table of Contents (23) Chapters Close

Preface 1. Prologue
2. Part 1:Introduction to Analytics Engineering FREE CHAPTER
3. Chapter 1: What Is Analytics Engineering? 4. Chapter 2: The Modern Data Stack 5. Part 2: Building Data Pipelines
6. Chapter 3: Data Ingestion 7. Chapter 4: Data Warehousing 8. Chapter 5: Data Modeling 9. Chapter 6: Transforming Data 10. Chapter 7: Serving Data 11. Part 3: Hands-On Guide to Building a Data Platform
12. Chapter 8: Hands-On Analytics Engineering 13. Part 4: DataOps
14. Chapter 9: Data Quality and Observability 15. Chapter 10: Writing Code in a Team 16. Chapter 11: Automating Workflows 17. Part 5: Data Strategy
18. Chapter 12: Driving Business Adoption 19. Chapter 13: Data Governance 20. Chapter 14: Epilogue 21. Index
22. Other Books You May Enjoy

Writing Code in a Team

Writing code individually and writing code in a team can be very different experiences. As your team grows, new challenges will arise from working together on the same code base. The most apparent challenges are about code quality, ownership, and accountability. Who works on what? How can you keep consistent coding styles and quality? How can you review code? How can you easily onboard new colleagues?

The clear separation of duties, version control, tracking issues and tasks, agreeing on and enforcing a coding standard, CI/CD, code reviews, and documentation are all ways to streamline maintaining a code base within a team.

Version control and CI/CD are tools and techniques that can help data professionals work more efficiently, collaborate more effectively, and maintain the integrity and reliability of their code and data assets over time. As analytics engineers, we should adopt these principles to make collaboration on the same code base more efficient...

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