Eneco powers its Toon connected home energy platform with Databricks ML
Eneco, a Dutch energy provider, migrated from an on-premises Hadoop cluster to the Databricks Data + AI Platform on AWS and Delta Lake to process over 300 types of sensor and user interaction data and petabytes of energy usage data from 350,000 households on its Toon connected home platform, building ML models that detect appliance energy usage patterns at 10-second intervals and alert users to heating system anomalies.
Overview
Eneco, a Dutch energy provider, migrated from an on-premises Hadoop cluster to the Databricks Data + AI Platform on AWS and Delta Lake to process over 300 types of sensor and user interaction data and petabytes of energy usage data from 350,000 households on its Toon connected home platform, building ML models that detect appliance energy usage patterns at 10-second intervals and alert users to heating system anomalies.
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Inspect the highlighted sourceThe challenge
Eneco's legacy on-premises Hadoop infrastructure was costly and complex to manage at scale, requiring data engineers to spend days ensuring new packages and dependencies were installed correctly; processing streaming IoT data reliably for ML was also a struggle, and data scientists relied on disparate, non-collaborative tools.
The solution
Eneco moved to the Databricks Data + AI Platform on AWS, storing large historical and streaming datasets in Amazon S3 via a common API and using Delta Lake to simplify data pipelines; collaborative notebooks let developers, engineers and data scientists build ML models that extract appliance energy-usage patterns at 10-second intervals and detect anomalies in home heating systems, alerting users with limited latency.
Reported business value
Eneco's Toon connected home platform now serves 350,000 households processing over 300 types of sensor and user interaction data, freeing data engineers from infrastructure management to focus on developing machine learning algorithms and giving users near-instant alerts to heating system anomalies before they affect comfort.
Sources
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