Case Study: Citi Bike achieves real-time, dataset-centric ride analytics with Preset

Citi Bike extends ride analytics beyond the last month with Preset and 5-minute GBFS data

Citi Bike, New York City's primary bike share system, faced a challenge in visualizing its data beyond the limitations of its historical monthly trip snapshots. They wanted to incorporate real-time General Bikeshare Feed Specification (GBFS) data to create more robust, up-to-date dashboards and analyses. To address this, they worked with Preset, utilizing its managed cloud service for Apache Superset, alongside dbt and Snowflake for data transformation.

Preset implemented a dataset-centric solution using dbt to model and transform the data within Snowflake. This involved ingesting the real-time GBFS feed, creating a derived dataset with pre-calculated aggregates, and surfacing it in Superset. This approach allowed Citi Bike to visualize key metrics, like total rides from a station, beyond the last date available in their historical data, effectively combining historical and real-time information for a more complete analytical picture.

Citi Bike