In immediately’s data-driven world, organizations rely closely on correct information to make vital enterprise selections. As a accountable and reliable Information Engineer, making certain information high quality is paramount. Even a short interval of displaying incorrect information on a dashboard can result in the speedy unfold of misinformation all through the whole group, very similar to a extremely infectious virus spreads by a residing organism.
However how can we forestall this? Ideally, we might keep away from information high quality points altogether. Nonetheless, the unhappy reality is that it’s unattainable to utterly forestall them. Nonetheless, there are two key actions we will take to mitigate the influence.
- Be the primary to know when a knowledge high quality difficulty arises
- Decrease the time required to repair the problem
On this weblog, I’ll present you methods to implement the second level straight in your code. I’ll create a knowledge pipeline in Python utilizing generated information from Mockaroo and leverage Tableau to rapidly determine the reason for any failures. Should you’re searching for another testing framework, take a look at my article on An Introduction into Nice Expectations with python.
