Systems | Development | Analytics | API | Testing

TL;DR: DreamFactory 7.4+ includes a built-in MCP (Model Context Protocol) server that lets you connect any LLM—ChatGPT, Claude, Perplexity, or custom AI agents—to your enterprise databases through governed, role-based APIs. Setup takes minutes: create an MCP service in the admin console, copy the OAuth credentials, and point your AI application to the generated endpoint.

As AI continues to rise in importance across all industries, the cost of implementation, readily available access to cloud computing, and practical business use cases make AI-powered offerings a competitive advantage for product managers, engineering, and data leaders. However, AI isn’t without its fair share of risks and challenges.

Artificial intelligence workloads are reshaping how memory is produced, priced, and prioritized. Not because the supply chain has fundamentally broken, but because manufacturers are making deliberate decisions about where to place capacity and capital. Wafer lines are being steered toward high-margin, long-term AI demand, not toward broad, undifferentiated expansion. HBM, advanced DRAM, and other AI-optimized memory now command the majority of investment and forward planning.

If you’re evaluating MCP servers for your analytics stack, you’ve probably noticed that “MCP support” can mean very different things depending on the vendor. I’ve been working with both platforms, and the distinction matters more than most comparison articles let on. Supermetrics and Databox both offer MCP implementations, but they’re built for different jobs.

Stop looking for an AI Analytics tool. Start looking for an analytics protocol. That advice sounds counterintuitive. Everyone’s searching for “the best AI analytics platform” or “which BI tool has the best AI.” But that framing misses what’s actually happening in the market, and why most AI analytics implementations fail to deliver on their promise.

The emergence of AI has created uncertainties in the software and technology world. As it encroaches into the conventional application test-automation space, QA professionals might feel threatened or even cornered. While it is true that AI is changing traditional testing roles, it also opened new opportunities in the data testing space. But what does AI rely on? Obviously, data!