Modernizing real-time data flows across six manufacturing plants
RabbitMQMQTTKafkaSQL Server
KubernetesThe proof of concept validated that the new architecture eliminates database CPU contention while maintaining sub-minute data delivery. The phased rollout across six plants is on track for 2026–2027, …
Overview
The company operates multiple manufacturing plants that rely on real-time data ingestion to drive production decisions. Their existing system used SQL Server triggers to capture database events and relay them downstream, but this approach was creating substantial CPU overload on production database servers and threatening operational stability.
Challenge
The SQL-trigger-based ingestion system was causing critical CPU contention on production databases, impacting real-time data delivery. Manufacturing operations require low-latency data flows — delays of more than a few minutes can trigger plant-floor alarms and even mandatory shutdowns. The existing worker middleware was difficult to maintain and lacked vendor accountability.
Environment
On-premises Rancher Kubernetes cluster, SQL Server databases, Ignition SCADA, multiple manufacturing execution systems across six plants, with a production rollout timeline extending to 2027.
Approach
AceMQ engaged in a phased consulting engagement beginning with a three-week discovery and architecture assessment. The team evaluated multiple messaging tools including RabbitMQ, MQTT, and Kafka, then designed and built a proof-of-concept demonstrating a shadow table pattern for non-blocking data extraction that eliminates database contention while maintaining real-time delivery.
Solution
- 1Designed a shadow table pattern enabling non-blocking data extraction from SQL Server without impacting production database performance
- 2Built an Ignition-to-RabbitMQ data flow demo to validate real-time event publishing
- 3Evaluated and recommended messaging tools (MQTT, Kafka, RabbitMQ) for different use cases across the plant floor
- 4Created an architecture blueprint for event-driven data ingestion across six plants
- 5Provided ongoing vendor-accountable support and maintenance strategy
Outcome
The proof of concept validated that the new architecture eliminates database CPU contention while maintaining sub-minute data delivery. The phased rollout across six plants is on track for 2026–2027, with AceMQ providing ongoing architecture advisory and implementation support.
Technologies
RabbitMQMQTTKafkaSQL ServerKubernetesRelated Use Cases
RabbitMQ Performance Tuning
Throughput, latency, and resource utilization optimization including queue design, publisher confirms, replication settings, and concurrency tuning.
RabbitMQ Integration Modernization
Decoupling legacy ERP and file-based processes with API enablement, middleware design, and asynchronous event flows.
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