Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need.
