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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

Optimizing Data Pipelines

The importance of data in companies has significantly increased the investments in data platforms by companies. Over time, this has increased companies’ priority of being aware of what their data pipelines do and how they do it and therefore monitoring not only the quality of the outcomes but also the state of health of the pipelines. At the same time, they are also monitoring the usage of the resources and tracking the associated costs.

In this chapter, we will understand how data observability offers us a way to make the governance of our data pipelines scalable and sustainable. First, we will focus on understanding the key data pipelines, their main components, and the types of data pipelines, as well as their characteristics. Then, we will learn how data observability and, in particular, data lineage can be used to manage several aspects of the data pipeline life cycle, such as the costs and the risks.

In this chapter, we’ll cover the...

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