Unlocking the Secrets of Client Data
summary
Shows concrete outcomes and configs: using DataDog install (npm/Yarn) to collect web vitals, runtime errors, asset sizes and custom events; triaging a 10 MB image regression back to a specific git commit; operating at scale with 75 micro frontends / 50 engineers and per‑service dashboards; alerting thresholds that catch slow page loads and a 10,000 requests/day API spike; cites business impact like Walmart’s $67M and Amazon’s 100 ms → 1% revenue rule and demonstrates logs, stack traces and the dashboard queries used.
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chapters (7)
- 0:00Performance impact on revenue
- 1:26Client application monitoring
- 3:37Front-end performance solutions
- 5:26Micro front-end monitoring
- 7:34Data analysis and correlations
- 10:25Proactive alerts and resolution
- 11:50Recap and key takeaways
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