Skip to main content

Coforge designs an unparalleled IoT data processing and analytics ecosystem for a US enterprise

Coforge designs an unparalleled IoT data processing and analytics ecosystem for a US enterprise

The Client

Our client is an American multinational discount store operator and one of the largest corporations in the global retail industry. It is the largest retailer in the world, with 10,623 stores and 380 distribution facilities in 27 countries. Its revenues since 2022 have exceeded $600 billion a year. More than 58% of its revenue comes from in-store grocery purchases.

Business Challenge

The client faced a critical challenge in managing and monitoring their cooking equipment, storage containers, and transport vehicles. With an extensive network of Internet of Things (IoT) sensors continuously streaming data on power, temperature, HVAC, and more, they needed a robust solution to process, enrich, and analyze this high-speed, low-latency data in real-time.

The client dealt with a staggering order of 100,000 messages per second from IoT devices, requiring constant monitoring of food storage and processing environments. The challenge was to ensure real-time parsing, enrichment, predictive monitoring, and continuous delivery of data to advanced analytics and data science users

Coforge Solution

To address these challenges, our team proposed a comprehensive solution:

Real-time Data Processing Layer:

  • Kafka: For high-throughput, fault-tolerant messaging.
  • Spark: To process and analyze streaming data in real-time.
  • Cassandra: For scalable and distributed data storage.
  • Redis: In-memory data store for caching and quick access.

Data Enrichment:

  • Adding master data attributes to enhance context.
  • Tagging for assets and processes.
  • Implementing business rules-based data quality checks.

Real-time Data Aggregation:

  • Calculated aggregate metrics.
  • Time window functions and moving averages.
  • Threshold checks for anomaly detection.
  • Enabling search and real-time dashboard functionality.

Analytics Layer:

  • Near-real-time data analysis.
  • Implementation of AI/ML models for predictive analytics.

Data Storage:

  • Historical data analysis stored on Cassandra for reference.


The implemented solution resulted in several positive outcomes:

Seamless Ingestion and Processing:

  • Efficient handling of 100,000 messages per second, ensuring seamless data ingestion and processing.

Technology-Agnostic Robust Architecture:

  • The architecture proved to be robust and technology-agnostic, providing high performance and scalability.

Near Real-time Enrichment and Analytics:

  • Real-time data enrichment and in-process analytics allowed for quick decision-making and monitoring through interactive dashboards.

Advanced Analytics with AI/ML:

  • The solution enabled advanced analytics using AI/ML models, providing predictive insights for proactive decision-making.


The implemented solution not only met but exceeded the client's expectations. It established a foundation for future scalability and innovation in the realm of real-time IoT data processing, enriching the food industry's monitoring capabilities in hot and cold environments.

Let’s engage