Real-Time Data Streaming and Analytics for a 3PL Provider
Client Overview
In Industry: Third-Party Logistics (3PL)
Location: United States
Operations: International freight management with multiple carrier partnerships
Business Challenge
The client faced multiple data-related operational inefficiencies:
- Data Fragmentation: Shipment, route, carrier, and delivery data were spread across systems and difficult to consolidate.
- Manual Dashboarding: Building dashboards required manual data export and cleanup, consuming hours or even days.
- Database Load Issues: High-volume data queries for analytics significantly impacted the performance of their MySQL transactional database.
- Lack of Real-Time Insights: Decisions were made on stale data due to batch-mode reporting.
Our Solution
We designed a streaming data analytics architecture to decouple reporting from the core operational database, deliver near real-time insights, and enable scalable dashboards
Key Components
Event-Driven Architecture
- Event Listeners captured changes from the MySQL database in real-time (e.g., new loads, route updates, carrier selection).
- Ensured minimal impact on transactional systems.
Kafka for Data Streaming
- Implemented Apache Kafka to stream event data asynchronously to downstream consumers.
- Supported horizontal scaling and high throughput for future growth.
MongoDB for Analytical Storage
- Used MongoDB to persist the streamed data in a schema-flexible, analytics-optimized format.
- Enabled fast, ad hoc queries without overloading production systems.
Custom Data Consumers
- Built reusable Kafka consumers tailored to process specific event types (e.g., delivery performance, load consolidation).
- Supported advanced business logic and transformations before data was stored or visualized.
Apache Superset for Dashboarding
- Integrated Superset to visualize MongoDB data through curated dashboards.
- Provided customers with powerful, self-service BI tools for exploring patterns and KPIs like:
- Load completion rate
- Delivery delays
- Carrier performance
- Consolidation efficiency
Business Impact
| KPI / Metric | Before Solution | After Implementation |
|---|---|---|
| Dashboard creation time | 3–4 days | < 10 minutes |
| MySQL DB query load | High | Reduced by 80% |
| Data freshness for insights | 1-day lag | Near real-time (<1 min delay) |
| Analytics adoption by ops team | Low (manual dependency) | High (self-service dashboard) |
| Decision-making lead time | Delayed by manual processes | Real-time |
Technologies Used
- Backend & Streaming: Apache Kafka, Java
- Databases: MySQL (source), MongoDB (analytics)
- Data Visualization: Apache Superset
- Infrastructure: Docker, AWS EC2/S3 (optional cloud deployment)