How to Instantly Detect Data Quality Issues and Identify their Causes
In today’s data-driven world, organizations rely heavily on accurate data to make critical business decisions. As a responsible and trustworthy Data Engineer, ensuring data quality is paramount. Even a brief period of displaying incorrect data on a dashboard can lead to the rapid spread of misinformation throughout the entire organization, much like a highly infectious virus spreads through a living organism.
But how can we prevent this? Ideally, we would avoid data quality issues altogether. However, the sad truth is that it’s impossible to completely prevent them. Still, there are two key actions we can take to mitigate the impact.
- Be the first to know when a data quality issue arises
- Minimize the time required to fix the issue
In this blog, I’ll show you how to implement the second point directly in your code. I will create a data pipeline in Python using generated data from Mockaroo and leverage Tableau to quickly identify the cause of any failures. If you’re looking for an alternative testing framework, check out my article on An Introduction into Great Expectations with python.
