●Node.js + Logistics

Logistics technology is fundamentally about real-time data flow: vehicles sending positions, drivers reporting status, documents triggering actions, and stakeholders demanding visibility. Node.js excels in exactly this domain—its event-driven, non-blocking architecture handles high-frequency data streams without the threading complexity of traditional approaches. Consider fleet tracking: a 1,000-vehicle fleet sending GPS updates every 30 seconds generates 33 updates per second continuously. Each update needs processing, storage, and potentially triggers notifications to multiple parties. Node.js handles this with a single process, using its event loop to interleave I/O operations efficiently. A blocking architecture would require complex threading, connection pooling, and careful deadlock avoidance. The integration story is equally compelling. Logistics runs on legacy protocols—EDI X12 for load tenders and invoices, proprietary carrier APIs, and decades-old warehouse systems. Node.js becomes the universal translator, receiving EDI files, transforming them to JSON, enriching with API data, and forwarding to modern applications. The npm ecosystem has parsers for logistics formats, and JavaScript's flexible typing handles the inconsistent data common in freight systems. For modern logistics applications, WebSockets enable the real-time visibility that shippers demand. A Node.js backend can maintain thousands of concurrent connections to tracking dashboards, pushing position updates, ETA changes, and exception alerts instantly. Traditional request-response architectures can't deliver this experience without expensive polling.

Logistics projects start with mapping the data flows. What systems generate data? What systems consume it? What transformations happen between? This produces an integration architecture that guides the entire project. Node.js often becomes the integration hub, connecting legacy systems to modern interfaces. For fleet tracking systems, I implement a streaming architecture from day one. GPS data flows through Node.js stream processors that validate, enrich (reverse geocoding, geofencing), and distribute updates. TimescaleDB stores the time-series data with automatic partitioning. Redis pub/sub pushes updates to connected dashboards. This architecture scales linearly—add more Node.js processors as fleet size grows. The driver mobile experience requires careful attention to offline scenarios. Cellular coverage in rural areas and warehouse interiors is unreliable. I implement offline-first patterns with local SQLite storage, queuing updates until connectivity returns. The sync logic handles conflicts gracefully—the system knows what data is authoritative (server) vs. local (user preferences). For EDI integration, I build adapters that abstract the complexity of X12 parsing. A clean internal API handles load tenders, status updates, and invoices. The EDI translation layer converts between these and the 997/204/214/210 transaction sets carriers expect. When a new carrier onboards, we add an adapter—not rewrite business logic. Testing logistics applications requires realistic data volumes. I use recorded GPS traces and historical EDI transactions to simulate production load. Integration tests verify end-to-end flows: load tender received → driver assigned → status updates sent → invoice generated.