In right now’s data-driven world, organizations rely closely on correct information to make crucial enterprise selections. As a accountable and dependable information engineer, making certain information high quality is paramount. Even when incorrect information seems on a dashboard for less than a brief time period, misinformation can rapidly unfold all through a corporation, just like how a extremely contagious virus spreads via a residing organism.
However how will you stop this? Ideally, you may keep away from information high quality points altogether. Nonetheless, sadly it’s not possible to utterly stop them. Nonetheless, there are two necessary actions we will take to cut back the impression.
- Be the primary to know when information high quality points happen
- Reduce the time required to resolve points
On this weblog, we’ll present you tips on how to implement the second level straight in your code. Create information pipelines in Python utilizing information generated from Mockaroo and leverage Tableau to rapidly establish the reason for failures. In case you are on the lookout for another testing framework, please check with my article. Introducing Great Expectations with Python.
