Key components of data observability

In this section, we will see some examples of data observability metrics that are collected from inside applications and issues that can be raised from such quality issues. We will focus on detecting issues and to do so, we are going to create visuals of data observability issues in a Jupyter notebook.

If you want to follow the example, you can find it in the Chapter2 section of the GitHub repository. The name of the notebook is Visualise_Observability_Issues.ipynb.

In this part, we will focus on a timeliness, a completeness, and an accuracy issue.

The dataset that we provide is a basic example of marketing and sales data. The data represents the orders made on a web shop and consists of the following fields:

• date: The date of the order
• guid: A unique ID for the order
• email: The email address linked to the order
• page_visited: The number of pages the customer visited on the website
• duration: How long the customer...