Case Study: Peloton Cycle achieves real-time business-level ride and infrastructure monitoring with Datadog

Peloton Cycle integrates IT and business-level custom metrics

Peloton Cycle, maker of connected indoor bikes with 22" Android tablets, used Datadog to monitor system health and surface business indicators like the number of live rides. They struggled to derive an accurate ride count from aggregate NGINX requests (background traffic and changing usage patterns skewed their estimates) and also needed reliable cross-machine monitoring of Redis and PostgreSQL replica/standby lag.

To solve this, Peloton rewrote their log parser to send all NGINX request counters to the local DogStatsD server with normalized URL tags, letting them track a ride-specific endpoint for an accurate live-ride metric, and built custom AgentCheck scripts that report replica/standby lag as gauges with alerts. The result was cleaner event streams, precise time-series ride counts (showing gaps when no rides occur), proactive replication-lag alerts, and company-facing dashboards that surface both system and business metrics.

Peloton

K.Z Win

DevOps Engineer