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

Static and dynamic elements

First, let’s focus on what we consider as a data observability element in this case. A data observability element is a piece of data you can retrieve from the running application that aims to make the pipeline observable. If it can be monitored, the same element can then become a SLI.

It’s important to make a clear distinction between two categories of observations: static and dynamic.

The set of static elements represents the assets, whereas the set of dynamic elements represents the usages of those assets. For instance, the application will be ranged in the static category, while the application will be run in the dynamic one.

The static elements correspond to all the observations that can be manually reported by a human documenting their data usage because they represent assets that are located and (virtually) accessible and can used or reused. Dynamic observations are often linked to the execution or usage of static elements and...