Book Image

Data Observability for Data Engineering

By : Michele Pinto, Sammy El Khammal
Book Image

Data Observability for Data Engineering

By: Michele Pinto, Sammy El Khammal

Overview of this book

In the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You’ll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you’ll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you’ll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.
Table of Contents (17 chapters)
1
Part 1: Introduction to Data Observability
4
Part 2: Implementing Data Observability
8
Part 3: How to adopt Data Observability in your organization
12
Part 4: Appendix

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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