A logistics client had 3 distribution centers with incompatible telematics providers. No unified fleet visibility existed. Data processing was batch-based with 2+ hour latency.
Architecture Overview
📊 Provider A→📊 Provider B→📊 Provider C
↓
🗄️ S3 Raw Data→⚙️ AWS Lambda (Python)→💾 RDS PostgreSQL→📈 Power BI Dashboard
Built an end-to-end serverless ETL pipeline using AWS services. Created a data normalization layer that transforms incompatible telematics data into a unified format.
Implemented Infrastructure as Code with CloudFormation for repeatable deployments. Set up CloudWatch monitoring and SNS alerts for pipeline health.
Results
Processing volume: 10,000+ daily records processed reliably
Latency: Reduced from 2+ hours to under 5 minutes
Normalization: Unified 3 incompatible telematics providers into single dashboard
Uptime: 99.9% with automated monitoring
Key Takeaway
The data normalization layer was the hardest but most valuable part. Standardizing at ingestion made everything downstream much simpler.