Systems | Development | Analytics | API | Testing

In today’s data-driven world, seamless data integration is crucial to ensuring the smooth operation of modern systems. With the growing complexity of distributed data platforms, businesses and developers are seeking efficient ways to move, process, and transform data. Apache Kafka has become the de facto standard for real-time data streaming, and Kafka Connect plays a key role in facilitating the integration of Kafka with various data sources and sinks.

Modern LLM coding agents are great at reading code, but they still make assumptions. When something breaks in production, those assumptions can slow you down—especially when the real issue lives in live traffic, API responses, or database behavior. In this post, I’ll walk through how to connect an MCP server to your LLM coding assistant so it can pull real production data on demand, validate its assumptions, and help you debug faster.

‍ The healthcare industry stands at the precipice of a revolution, shifting away from reactive, hospital-centric care towards proactive, personalised, and remote management. At the heart of this transformation is Remote Patient Monitoring (RPM), a system that uses connected digital health technologies to gather and transmit patient physiological data outside of traditional clinical settings. While RPM offers tremendous value, it generates a massive, continuous stream of data.

Developing Remote Patient Monitoring Software is a strategic investment that acts as a bridge between home care and clinical settings. Here are the essential highlights that will help you navigate through the blog. ‍ Driven by the need for continuous care and better patient engagement, healthcare delivery is rapidly shifting beyond hospital walls.

Modern DevOps observability tools are excellent for monitoring system health, tracking distributed traces, and aggregating metrics. However, they lack the fidelity needed for full-text search across application traffic. While observability platforms excel at showing what happened and when, they often fall short when you need to find where a specific piece of data (like an email address, user ID, or transaction token) appears as it flows through your entire application stack.