Use Case — Event-Driven Architecture
Batch processing tells you what happened yesterday. Real-time event-driven architecture tells you what's happening now — and lets your systems respond before the moment passes. When a customer transaction fires, when a sensor reads an anomaly, when an order changes status — the event-driven pattern captures that signal and routes it to every system that needs to act on it, in milliseconds, at any scale.
This is architecture we have designed, built, and operated at scale — from a near real-time POS integration system at H-E-B processing 150,000 messages per minute across thousands of store terminals, to Kafka-based event streaming at a 12-million-user consumer platform enabling real-time data flows across the entire organization, to a cloud analytics platform processing biometric telemetry from Garmin wearables with event detection and client-facing dashboards. The pattern scales from startup to enterprise.
Modern AI and agentic systems depend on event-driven foundations. An AI agent that responds to a customer action, triggers a workflow on a sensor reading, or personalizes an offer at the moment of transaction — all of that requires the event-driven plumbing underneath. We build both the foundation and the intelligence that runs on top of it.
Why It Matters
Most data pipelines are batch jobs — they run nightly, or hourly, and move data from where it's generated to where it's needed. That model worked when business moved slowly. It breaks when your customers expect real-time response, your operations need instant alerting, or your AI systems need current data to make useful decisions.
What We Build
Apache Kafka-based event streaming architectures that ingest, route, and process data in real time — replacing nightly batch jobs with continuous data flows that keep every downstream system current. Designed with hot, warm, and cold storage tiers that balance performance and cost across the medallion architecture.
High-throughput in-memory processing architectures for workloads where latency matters most — real-time offer delivery, transaction processing, and session-level personalization that can't wait for a database round-trip. Built using TIBCO ActiveSpaces, Apache Ignite, and cloud-native equivalents depending on the stack.
Event processing engines that detect patterns across streams of events — not just individual events, but sequences, correlations, and temporal patterns that indicate a condition worth acting on. Equipment behavior that predicts failure, customer actions that signal churn, transaction patterns that suggest fraud.
Event-driven personalization engines that integrate with POS, e-commerce, and mobile transaction streams to deliver offers, rewards, and recommendations at the point of transaction — not in the next marketing batch. Built on in-memory architectures that handle peak transaction volumes without degradation.
Scalable ingestion pipelines for high-frequency sensor data, wearable telemetry, and edge device streams — processing continuous data from physical systems into analytical and operational applications. Designed for utility infrastructure, manufacturing equipment, fleet telematics, and health tech applications.
The architectural foundation for agentic AI systems — event topics, subscriptions, and routing patterns that let AI agents subscribe to relevant events, evaluate context, and trigger actions without polling or batch triggers. The difference between an AI system that responds to your business and one that waits to be asked.
Proven at Scale
At H-E-B — a $38 billion Texas retailer — we designed and built a near real-time POS integration system processing up to 150,000 messages per minute across thousands of store locations. Every transaction fired an event that fed downstream business intelligence, replenishment systems, and real-time offer and rewards delivery. The in-memory data grid, built on TIBCO ActiveSpaces and BusinessEvents, enabled real-time customer rewards at the point of sale — a capability that batch architecture fundamentally cannot support.
We also designed the self-healing monitoring infrastructure for this system — custom TIBCO Hawk rulebases and MQ MicroAgent configurations that provided real-time monitoring, auto-recovery, and self-healing capabilities across the distributed architecture, reducing manual intervention and maintaining the uptime that a retail operation at this scale requires.
At Slickdeals, we migrated the data platform to an AWS-based event-driven architecture with Kafka at its core — operationalizing events across the organization and enabling real-time data flows that the prior batch-based SQL Server environment couldn't support. The event-driven foundation became the basis for the ML personalization and real-time analytics that differentiated the platform's product experience.
At HeartSync, we architected a cloud analytics platform that processed real-time biometric telemetry from Garmin wearable devices — ingestion pipelines, event detection algorithms, and client-facing dashboards that translated continuous sensor streams into actionable population-level insights. The platform was acquired by the City of Windcrest, TX.
Let's Talk
Tell us about your current data pipeline architecture, the latency you're living with, and what real-time event response would unlock for your business.