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

Defining the data observability context

Following the data observability principles, the context of data manipulation is important. Now is a good time to define what we mean by context in data observability. We can define the context as the set of circumstances of the data transformations – in other words, they are the metadata that can help you understand how and where the data transformation or manipulation happened. The context will tell you which application manipulated the data, when it was manipulated, who executed the manipulation, what triggered it, and so on. This context should give you all the necessary pieces of information while you’re debugging the code or the data issue, both upstream (root cause analysis) and downstream (impact analysis).

Long story short, the context is the background of the application. It starts at the beginning of the script or program execution and lasts until all the data transformations the application was supposed to perform...