Manufacturer Boosts Uptime

Challenges

Their sensor data was trapped in legacy historians and streaming to multiple incompatible systems. Maintenance followed fixed schedules, leading to over-servicing or unexpected failures. Engineers lacked anomaly alerts and had no historical analytics. The data science team couldn’t process large volumes of time-series data or retrain models due to infrastructure limitations. Plant managers needed intuitive dashboards to monitor asset health, but latency and data inconsistencies caused frequent outages.

• Sensor data fragmented across SCADA, historians, and Excel exports
• Predictive maintenance wasn’t possible—only scheduled/reactive servicing
• Engineers had no visibility into real-time anomalies
• ML workflows for time-series modeling were too slow and brittle
• Plant managers lacked accessible dashboards for daily ops
• IT teams couldn’t scale model deployment across plants

Solutions

We implemented a unified Databricks Lakehouse architecture to consolidate batch and streaming data for ML-based asset intelligence.

Ingestion: Used Apache Kafka and MQTT brokers to stream IoT data from edge devices to Databricks

Delta Lake: Stored structured time-series data in Delta format for fast querying and version control

ML Pipelines: Trained predictive maintenance models in PySpark/TensorFlow, tracking vibration, voltage, and thermal patterns

Model Ops: Managed model lifecycle with MLflow, including retraining, drift alerts, and versioning

Dashboards: Built Grafana dashboards for plant managers and alerts pushed via Microsoft Teams

Cost Optimization: Used autoscaling compute clusters and Delta cache to optimize performance and cost