Analyze and visualize Strava activity details with the Elastic Stack
Blog post from Elastic
Philipp Kahr's blog post explores how to analyze and visualize Strava activity data using the Elastic Stack, building on an earlier post about importing Strava data into this platform. Strava, a popular app for athletes to track and share activity data, provides detailed metrics through its API, such as time, distance, heart rate, and more, which are extracted into a usable format for Elasticsearch. By running a Python script, the data is restructured from a JSON array into individual documents for aggregation and analysis. This transformation allows users to perform detailed analyses, such as examining correlations between heart rate and speed or cadence and gradient. Visualization tools like histograms and Lens enable users to explore their fitness data, offering insights into workout intensity and patterns. The blog encourages readers to try these techniques to gain a deeper understanding of their fitness data and introduces the Elastic Cloud for further exploration.
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