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Data Observability for Data Engineering

You're reading from   Data Observability for Data Engineering Proactive strategies for ensuring data accuracy and addressing broken data pipelines

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781804616024
Length 228 pages
Edition 1st Edition
Languages
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Authors (2):
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Michele Pinto Michele Pinto
Author Profile Icon Michele Pinto
Michele Pinto
Sammy El Khammal Sammy El Khammal
Author Profile Icon Sammy El Khammal
Sammy El Khammal
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Introduction to Data Observability
2. Chapter 1: Fundamentals of Data Quality Monitoring FREE CHAPTER 3. Chapter 2: Fundamentals of Data Observability 4. Part 2: Implementing Data Observability
5. Chapter 3: Data Observability Techniques 6. Chapter 4: Data Observability Elements 7. Chapter 5: Defining Rules on Indicators 8. Part 3: How to adopt Data Observability in your organization
9. Chapter 6: Root Cause Analysis 10. Chapter 7: Optimizing Data Pipelines 11. Chapter 8: Organizing Data Teams and Measuring the Success of Data Observability 12. Part 4: Appendix
13. Chapter 9: Data Observability Checklist 14. Chapter 10: Pathway to Data Observability 15. Index 16. Other Books You May Enjoy

Technical roadmap to include data observability

In this section, we will describe how you can add step-by-step observability to your data applications while summarizing all the elements covered in this book. We will see what we need to consider when starting with data observability from a technical point of view. We will cover the following topics:

  • Allocating the right resources to your data observability project
  • Defining clear objectives with the team
  • Implementing data observability in applications
  • Continuously improving observability
  • Scaling data observability

Let’s get started!

Allocating the right resources to your data observability project

To implement data observability in your data applications, allocating the right resources is key to success. Such resources include budget, staffing, tools, and time.

Budget

First of all, defining a budget for data observability can be tricky. From our experience, we often see this budget as...

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