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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

Project – continuous validation of the data

Now that we have learned how to define the SLOs of our projects and how to transform these SLOs into rules, it is time to learn how to integrate these rules into a CI/CD process and how to implement an end-to-end data validation pipeline.

Concepts of CI/CD

For several years now, software development has adopted a set of best practices called CI/CD, which is aimed at eliminating the distance that exists between development and operations activities. This objective is mainly realized by forcing teams to automate the building, testing, and deployment phases of applications.

The acronym CI/CD stands for continuous integration (CI) and continuous delivery (CD), or in some cases, continuous deployment (CD). Before introducing the concept of continuous data validation, it is important to understand these concepts in detail.

In Figure 5.2, we can see a graphical representation of the main stages of these processes:

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