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common.homecaseStudies.titleLogistics Scale-up
Logistics

Built Real-Time IoT Pipeline for 10K+ Fleet Sensors

4 weeks
3 caseStudies.team
Logistics Scale-up
AWSMSK (Kafka)KubernetesGrafanaTimescaleDB
2M
Events/min
800ms
End-to-End Latency
4TB
Daily Data
Zero
Data Loss
Pipeline Transformation
Metric
Before
After
Processing
Batch (15 min)
Stream (800ms) 1,125x faster
Events/min
~10K batch
2M real-time 200x more
Data Loss
Peak overflow
Zero Eliminated
Fleet Visibility
15 min delay
Real-time 100%
Event Pipeline Architecture
1
IoT Sensors
10K devices
2
MSK (Kafka)
6 brokers
3
K8s Consumers
Auto-scaled
4
TimescaleDB
Time series

caseStudies.theChallenge

Fleet management company needed real-time telemetry from 10K IoT sensors. Legacy batch processing had 15-minute delay, making route optimization impossible.

✗10,000 IoT sensors generating 2M events/minute
✗Legacy batch processing with 15-minute delay
✗No real-time visibility into fleet location or status
✗Data loss during peak hours due to queue overflow

caseStudies.ourSolution

Event-driven architecture with Amazon MSK (Kafka), Kubernetes consumer groups, TimescaleDB for time-series storage, and Grafana for real-time dashboards.

Deployed 6-broker MSK cluster with 3x replication
Built Kubernetes consumer groups with auto-scaling
Implemented TimescaleDB hypertables for efficient time-series queries
Created Grafana dashboards with sub-second refresh
Set up dead-letter queues for failed event processing
caseStudies.resultsImpact

Real-time fleet tracking enabled 23% fuel cost reduction through route optimization.

caseStudies.projectTimeline

Week 1
Architecture design, MSK cluster provisioning
Week 2
Consumer group development, TimescaleDB setup
Week 3
Grafana dashboards, alerting rules
Week 4
Load testing at 3M events/min, production cutover
C

"CloudLink built our real-time tracking pipeline in 4 weeks. Sub-second location updates changed our business."

CTO
Logistics Scale-up

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