Why Your Data Pipelines Need Closed-Loop Feedback Control
<p><em>As data teams scale up on the cloud, data platform teams need to ensure the workloads they are responsible for are meeting business objectives, </em><a href="https://www.synccomputing.com/" rel="noopener ugc nofollow" target="_blank"><em>our main mission here at Sync</em></a><em>. At scale with dozens of data engineers building hundreds of production jobs, controlling their performance at scale is untenable for a myriad of reasons from technical to human.</em></p>
<p>The missing link today is the establishment of a <a href="https://en.wikipedia.org/wiki/Closed-loop_controller" rel="noopener ugc nofollow" target="_blank">closed loop feedback system</a> that helps automatically drive pipeline infrastructure towards business goals. That was a mouthful, so let’s dive in and get more concrete about this problem.</p>
<p><strong>The problem for data platform teams today</strong></p>
<p>Data platform teams have to manage fundamentally distinct stakeholders from management to engineers. Oftentimes these two teams have opposing goals, and platform managers can be squeezed by both ends.</p>
<p><a href="https://towardsdatascience.com/why-your-data-pipelines-need-closed-loop-feedback-control-76e28e3565f"><strong>Website</strong></a></p>