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 transient period of displaying incorrect data on a dashboard can result in the rapid spread of misinformation throughout all the organization, very similar to a highly infectious virus spreads through a living organism.
But how can we prevent this? Ideally, we might avoid data quality issues altogether. Nevertheless, the sad truth is that it’s inconceivable to completely prevent them. Still, there are two key actions we are able to take to mitigate the impact.
- Be the primary to know when a knowledge quality issue arises
- Minimize the time required to repair the problem
On this blog, I’ll show you methods to implement the second point directly in your code. I’ll create a knowledge pipeline in Python using generated data from Mockaroo and leverage Tableau to quickly discover the explanation for any failures. In the event you’re in search of another testing framework, try my article on An Introduction into Great Expectations with python.