How you can Immediately Detect Knowledge High quality Points and Determine their Causes
In at this time’s data-driven world, organizations rely closely on correct information to make important enterprise selections. As a accountable and reliable Knowledge Engineer, making certain information high quality is paramount. Even a quick interval of displaying incorrect information on a dashboard can result in the speedy unfold of misinformation all through the complete group, very similar to a extremely infectious virus spreads via a residing organism.
However how can we stop this? Ideally, we’d keep away from information high quality points altogether. Nonetheless, the unhappy reality is that it’s unimaginable to fully stop them. Nonetheless, there are two key actions we will take to mitigate the affect.
- Be the primary to know when an information high quality difficulty arises
- Reduce the time required to repair the difficulty
On this weblog, I’ll present you how one can implement the second level immediately in your code. I’ll create an information pipeline in Python utilizing generated information from Mockaroo and leverage Tableau to rapidly establish the reason for any failures. Should you’re in search of an alternate testing framework, take a look at my article on An Introduction into Nice Expectations with python.
