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Fundamentals of Analytics Engineering

You're reading from  Fundamentals of Analytics Engineering

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781837636457
Pages 332 pages
Edition 1st Edition
Languages
Authors (7):
Dumky De Wilde Dumky De Wilde
Profile icon Dumky De Wilde
Fanny Kassapian Fanny Kassapian
Profile icon Fanny Kassapian
Jovan Gligorevic Jovan Gligorevic
Profile icon Jovan Gligorevic
Juan Manuel Perafan Juan Manuel Perafan
Profile icon Juan Manuel Perafan
Lasse Benninga Lasse Benninga
Profile icon Lasse Benninga
Ricardo Angel Granados Lopez Ricardo Angel Granados Lopez
Profile icon Ricardo Angel Granados Lopez
Taís Laurindo Pereira Taís Laurindo Pereira
Profile icon Taís Laurindo Pereira
View More author details

Table of Contents (23) Chapters

Preface 1. Prologue
2. Part 1:Introduction to Analytics Engineering
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

Tracking tasks and issues

If the roles have been defined and assigned within a team, using issue and task-tracking tools with an Agile methodology, such as Scrum, can be an effective way to increase productivity within a team. We are not going to talk about Agile methodologies, but we encourage reading up on this topic, as doing task tracking and working in an agile way are quite related.

With the help of task and issue tracking tools, developers and project managers can collaborate more effectively, identify and address issues early on, and ultimately deliver high-quality data products that meet the needs of their users.

This sounds evident; however, in our experience as consultants, we have noticed that teams across different industries have implemented this practice to varying degrees. We have seen it all, from teams who do not do any issue and task tracking to teams who use the latest tools but have no coherent strategy for using these tools, and, thus, the tool quickly becomes...

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