Introduction
A rice manufacturing giant operating a multi-stage processing plant — spanning Pre-Steaming, Soaking, Post-Steaming, Tempering, and Drying — generated high-volume IoT sensor data measuring temperature, moisture, flow, pressure, and valve operations. While IoT infrastructure was in place, the client lacked a scalable ingestion architecture, stable CDC management, and a centralized analytics layer. Innovatics delivered an end-to-end Industrial IoT Data Platform to convert raw telemetry into actionable operational intelligence.
Challenges
- Historical data replays into BigQuery created duplicate partitions and increased egress and storage costs significantly
- Broken CDC window logic halted downstream pipelines
- No centralized plant-wide dashboard or deviation-based monitoring in place
- Batch comparisons were performed manually
- Equipment downtime and sensor alerts were not correlated with production data
- Temperature, moisture, and cycle times varied across batches without structured deviation tracking
Solution
- Designed controlled schema evolution handling to prevent CDC instability and eliminate duplicate partition creation
- Prevented historical data replay during new tag additions while maintaining CDC continuity
- Stabilized BigQuery cost spikes through ingestion validation and logging mechanisms
- Built a batch-stage dimensional schema with structured time-series transformations
- Engineered a deviation-based KPI framework and linked sensor telemetry with equipment uptime metrics
- Delivered batch performance analytics with stage-wise time and temperature monitoring
- Enabled cross-tank and product benchmarking along with Downtime Pareto analysis
- Set up critical alert correlation across equipment and sensor health dashboards
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