Case Study: Strava achieves self-serve analytics and continuous improvement at scale with Snowplow

How Strava drives a culture of continuous improvement with self-serve behavioral data from Snowplow

Strava, the global athletic platform with 64 million active users and over 40 million activities uploaded weekly, faced the challenge of capturing, modeling and delivering enormous volumes of event data (typically ~3 billion events/day, peaking at 4.4 billion) to analysts while keeping costs and headcount under control. Disparate data sources and difficult tracking on new features created analytics blind spots that threatened Strava’s goal of a culture of continuous improvement, so they turned to Snowplow for a scalable behavioral-data solution.

Snowplow provided an open, flexible data pipeline and the Snowplow Insights managed service, enabling Strava to collect and process over 4 billion events per day, standardize tracking, and give analysts self-serve access (using derived tables in Snowflake) to instrument end-to-end analytics without heavy engineering support. The result was a democratized data culture, deeper insights and improved analytics coverage, while Snowplow’s managed approach proved more cost-effective than hiring additional engineers and freed Strava’s data team to focus on higher-value projects.

Strava

Daniel Huang

Data Engineer