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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

Data Observability Checklist

We have seen that data observability has the capability to become an important component in your data projects. It enables organizations to gain real-time insights into the behavior of their systems and infrastructure, allowing for faster troubleshooting, improved performance, and better decision-making.

However, implementing data observability can also present a number of challenges and pitfalls. After having conducted several data observability projects across different industry sectors, we wanted to share with you some points of attention to make your journey toward data observability successful.

In this chapter, we will explore the perks and drawbacks of data observability, providing guidance on how to implement it effectively while avoiding common pitfalls. We will see how data observability projects may fail, and what the strategies to overcome this are.

By the end of this chapter, you will have a deeper understanding of the challenges of...

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