Logistics plays a crucial role in the success of businesses across various industries. It involves the coordination and management of goods, services, and information from point of origin to consumption. This case study focuses on a logistics company which faced challenges in its daily operations, such as rider management to drive accuracy, efficiency and seamless operations to support its e-commerce and q-commerce clients.
Over 30,000 cross vendor OT devices generated massive volumes of data, with varying formats and structures that needed normalization.
Sending all data to the cloud for processing would have resulted in high latency and potential data loss.
Edge devices had limited computing resources compared to centralized cloud servers, requiring efficient orchestration and resource usage.
The solution needed to scale horizontally to accommodate future integrations and increased data volume.
RKE was installed on edge datacenter to provide a consistent Kubernetes platform for orchestrating containerized applications.
The small footprint of RKE ensured low resource usage, making it suitable for resource-constrained environments.
A containerized data pipeline was deployed on the RKE clusters to discover, ingest, preprocess, and normalize OT data locally.
The pipeline utilized various microservices for data ingestion, transformation, and storage.
OT devices communicated directly with the edge computing nodes, reducing data transmission latency.
A buffer mechanism handled intermittent connectivity, ensuring that no data was lost during network outages.
Incoming data was normalized into a consistent format through preprocessing microservices.
Aggregation microservices further reduced data size by filtering noise before sending to centralized servers for analysis.
The edge computing solution was designed to scale horizontally by adding more nodes as needed.
Centralized monitoring and logging allowed the operations team to track edge node performance and quickly respond to issues. 
Local data preprocessing and aggregation at the edge reduced the overall volume of data transmitted to the cloud.
On-premises data normalization minimized latency, allowing faster decision-making for time-sensitive operations.
Local data preprocessing and aggregation at the edge reduced the overall volume of data transmitted to the cloud.
On-premises data normalization minimized latency, allowing faster decision-making for time-sensitive operations.
Local data preprocessing and aggregation at the edge reduced the overall volume of data transmitted to the cloud.
The enterprise successfully leveraged Rancher Kubernetes Engine for efficient edge computing, enabling data normalization and aggregation from over 30,000 OT systems. The solution, which was processing 1500 messages per second, minimized data transmission latency and improved resilience while providing a scalable and consistent platform for containerized applications. This approach set a foundation for future expansion and optimized data management at the edge.
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