ROUVY: Rebuilding Data Infrastructure with Rittman Analytics

The Download

ROUVY is the leading realistic indoor cycling app that combines technology and sport to let cyclists ride real video routes from around the world. Offering over 1,300 routes and essential training features, the app is popular among both hobby riders and professional cyclists.

Its mission to create an authentic, immersive cycling experience has led to partnerships with global cycling brands like Lidl-Trek, La Vuelta, and Strava.

A company with over 100 employees, ROUVY is headquartered in Prague, Czechia, and was recognised as one of the 50 fastest-growing tech companies in Central Europe by Deloitte Technology Fast 50 CE in 2023.

Legacy Data Stack

Before the transformation, ROUVY relied on a mix of legacy systems, including a proprietary transformation engine, GoodData, Airflow, and an initially disconnected Chargebee system used for handling subscription management.

These systems struggled to meet the growing demands of ROUVY's platform, compounded by inefficient event tracking and data integration processes.

Challenges

• Custom Connectors: Data extraction relied on custom Python code, making updates and additions difficult.
• Transformation Engine Performance Issues: Struggled with large datasets, causing extended processing times and inconsistent metrics.
• GoodData Limitations: Memory-intensive operations and limited custom visualisation capabilities slowed data availability.
• Airflow Scalability Issues: Limited scalability and real-time data processing capabilities.
• External Dependencies: Heavy reliance on external consultants led to weaker internal documentation and challenging refactoring processes.
• Event Tracking and Data Loss: Significant gaps in event tracking with Google Analytics, leading to data loss and lack of control over event quality across ROUVY's first-party data.

Impact

• Delayed Decision-Making: Slower insights affected business agility.
• Increased Costs: Frequent reliance on external resources for maintaining and updating BI systems added significant costs.
• Customer Data Insights: Poor tracking and inconsistent data handling impacted marketing strategies and product engagement.

Transformation with Rittman Analytics

The primary goals were to address performance issues, create a consistent data pipeline, democratise data access, and build a scalable infrastructure using the following data stack:

• Dagster for Data Orchestration: Enhanced scalability and reduced processing times.
• Looker for BI and Analytics: Centralised data access, empowering teams with reliable analytics tools.
• Fivetran: For reliable data handling for critical sources.
• Airbyte: For cost-effective connecting of multiple sources, including Chargebee.
• dbt Core: Provided a robust transformation layer, enabling efficient and scalable data transformations.
• Snowplow: Improved behavioural data collection and event tracking quality.

Outcome

To transform their data analytics capabilities, ROUVY partnered with Rittman Analytics and Snowplow over a six-month period. Rittman Analytics was instrumental in designing the architecture of the new data stack and providing senior engineering support.