Build production-ready real-time clinical decision support systems using Apache Kafka and FHIR. Includes complete .NET/Python code, architecture patterns, and lessons from processing millions of healthcare events per day.
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Why Kafka Became the Backbone of Modern Data Architecture: Lessons from Building Event-Driven Systems at Scale
When LinkedIn open-sourced Kafka in 2011, few predicted it would become the de facto standard for real-time data streaming. Fourteen years later, Kafka processes trillions of messages daily across organizations of every size, from startups to Fortune 500 companies. Having architected event-driven systems for over two decades, I’ve watched Kafka evolve from an interesting alternative […]
Read more →Agent Memory Patterns: Building Persistent Context for AI Agents
Introduction: Memory is what transforms a stateless LLM into a persistent, context-aware agent. Without memory, every interaction starts from scratch—the agent forgets previous conversations, learned preferences, and accumulated knowledge. But implementing memory for agents is more complex than simply storing chat history. You need short-term memory for the current task, long-term memory for persistent knowledge, […]
Read more →Agentic AI Explained: Building Autonomous Systems That Plan, Act, and Learn
Move beyond simple chat to autonomous AI agents. Understand ReAct, multi-agent architectures, memory systems, and what actually works in production today.
Read more →FHIR API Security Part 2: Implementation & Best Practices
DPoP Implementation (.NET) Production DPoP Client (.NET) ⚠️ WARNING Production Security: Never hardcode credentials in your DPoP implementation. Use environment variables, Azure Key Vault, or AWS Secrets Manager. Rotate certificates every 90 days. Monitor for certificate expiration—a single expired cert can take down your entire API. FAPI 2.0 Authorization Flow 💡 TIP AI Agent Best […]
Read more →The Python Renaissance: Why 2025 Is the Year Everything Changed for Data Engineers
🎓 AUTHORITY NOTE This analysis draws from 20+ years of Python experience in enterprise data engineering, covering production deployments at scale across multiple Fortune 500 companies. Executive Summary Something remarkable happened in the Python ecosystem over the past year. After decades of incremental improvements, we’ve witnessed a fundamental shift in how data engineers approach their […]
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