What I Learned From Reading 50 Data Pipeline Postmortems
An analysis of 50 data pipeline postmortems reveals recurring failure patterns across major tech companies. Most of these failures are preventable at the design stage rather than during operations. The study highlights issues such as schema drift and load spikes that frequently lead to data loss or corruption.
- ▪Four common failure patterns were identified in the postmortems from companies like Uber and Netflix.
- ▪Schema drift was the most prevalent issue, occurring in 38% of incidents.
- ▪Many failures could have been avoided with better design practices rather than operational fixes.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3880780) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Andrew Tan Posted on May 19 • Originally published at layline.io What I Learned From Reading 50 Data Pipeline Postmortems #dataengineering #kafka #softwareengineering #data After analyzing 50 public postmortems from Uber, Netflix, Stripe, and others, four failure patterns emerge again and again. Most of them are preventable at the design stage. The postmortem paradox Every major tech company publishes them now. Stripe has a status page full of them.
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