Case Study: Enpal reduces data processing costs and scales smart energy data with MongoDB Atlas

Enpal cuts data processing costs by 60% with MongoDB

Enpal, a German solar company aiming to accelerate Europe's green energy transition, faced a massive data challenge. They needed to manage real-time data streams from over 65,000 customers' solar panels, batteries, and EV chargers to build a virtual power plant, but their initial architecture could not scale efficiently.

Using MongoDB Atlas and its Time Series Collections, Enpal created a scalable and efficient "hot storage" layer for their device data. This solution from MongoDB streamlined data querying and simplified compliance through features like sharding. The results were a 60% drop in data processing costs, the ability to scale to 100,000 devices on a single cluster, and the generation of accurate data for energy tracking and revenue.

Enpal