Systems | Development | Analytics | API | Testing

Somehow, another year has passed and we’re back again to deliver our Trends Outlook for 2026. As Qlik’s first milestone of the year, it’s a busy but exciting time that hugely energizes me after the holiday break. We weren’t short of inspiration. The world has given us a far from a blank canvas on which to illustrate our Trends and in particular, conversations around AI have firmly evolved from future-gazing to wrestling with the formula for harnessing its power.

Why “standalone AI” falls short AI works best when embedded in a process Guardrails are not a limitation—they’re an enabler Process-driven AI in action How leaders should be thinking about AI now Artificial intelligence is no longer optional in state and local government—it’s essential for meeting public expectations amid staffing shortages and budget constraints. AI is showing up across agencies and programs.

When you’re building realtime systems, blind spots slow you down. The new dashboard gives developers self-serve visibility into everything happening inside their apps, from high-level usage to individual connections, channels and messages. No setup. No external tools. Just open your browser and observe your data in motion.

Enterprises have invested decades of development effort into SQL Server stored procedures. These procedures contain critical business logic—validation rules, complex calculations, transaction handling, reporting queries—that applications need to access. But stored procedures weren't designed for modern API-driven architectures.

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.