Case Study: Acond achieves real-time anomaly detection and predictive maintenance with Databricks

Acond streamlines anomaly detection with the Databricks Platform

Acond, a European heat pump manufacturer, needed a better way to manage and analyze data from thousands of connected heat pumps. Delays in data analysis and limited visibility into device performance were making it harder to detect issues early and optimize service, so Acond turned to the Databricks Data Intelligence Platform.

Using Databricks with support from DataSentics, Acond built a unified IoT data platform on Microsoft Azure that combines historical and real-time data from more than 5,000 devices and dozens of sensors. The solution enabled real-time adjustments, anomaly detection, and remote predictive maintenance, improving efficiency, scalability, and customer satisfaction while processing over 30 million new rows of data per day and more than 1 billion historical records.

Acond

Viktor Šobek

R&D Manager