A Major US Retail Chain Achieved 5x Faster IoT Data Processing Using Real-Time Analytics Platform
Overview
A major US-based retail chain specializing in food and consumer goods operations. The client manages extensive cooking and storage equipment across multiple outlets, generating high-velocity IoT data requiring near real-time processing for operational efficiency and compliance.
The client faced challenges in processing over 100K IoT messages per second from diverse sensors and devices. Key issues included:
Inability to process real-time data efficiently, impacting timely decision-making.
Operational inefficiencies and equipment downtime due to lack of predictive insights.
Compliance and food safety risks arising from delayed data traceability and monitoring.
Solution
Coforge implemented a real-time IoT analytics platform leveraging Kafka, Spark, Cassandra, and Redis for low-latency data processing and intelligent analytics:
Ingested high-velocity IoT data using 100+ Kafka streams integrated with Spark for real-time decision-making.
Enriched data using 50+ business rules for quality checks, process tagging, and metadata management via Redis Cache, Spark, and MDM tools.
Designed 25+ aggregation metrics leveraging advanced windowing and thresholding techniques to power 15 real-time monitoring dashboards.
Stored historical IoT data in Cassandra, enabling scalable access for performance trend analysis and compliance reporting.
Integrated AI-driven anomaly detection and predictive ML models to optimize equipment usage, minimize downtime, and maintain food safety standards.
The Impact
5x faster IoT data processing, enabling real-time monitoring of cooking and storage systems.
50% faster operations and 35% improved predictive accuracy through AI-driven forecasting.
25% boost in compliance metrics, enhancing adherence to food safety standards and customer trust.
Significant improvement in product quality and operational reliability across retail outlets.
