Systems | Development | Analytics | API | Testing

Zero-ETL Database APIs let you access live data instantly without needing traditional ETL processes. Instead of extracting, transforming, and loading data, these APIs query databases directly in real-time, significantly reducing delays that can span hours. Key features include federated querying (accessing multiple data sources simultaneously) and schema-on-read (applying schemas dynamically during queries).

AI connectivity is enterprise infrastructure that governs, secures, routes, observes, and optimizes all AI interactions—providing the control layer organizations need to transform experimental AI into production-ready systems.

Recent outages tied to AI-assisted code changes have pushed companies into a corner. After several incidents with massive “blast radius” impacts, organizations like Amazon introduced stricter controls—mandating that senior engineers manually review all AI-generated code before it hits production. That response makes sense on paper, but it exposes a fatal flaw in the modern development pipeline.

DreamFactory 7.4.4 is a significant release for teams connecting AI agents to enterprise databases through the Model Context Protocol (MCP). The new _spec endpoint gives LLMs a complete understanding of any database schema in a single API call. Custom MCP tool definitions let admins extend their MCP server beyond built-in database operations. And new per-tool toggle controls with role-based service discovery bring the governance enterprises need before deploying AI-database integrations to production.

The European healthcare landscape is undergoing its most significant digital transformation in decades. We are moving away from a fragmented era where health data was locked within the walls of individual hospitals and national borders. In its place, the European Health Data Space (EHDS) is emerging, a unified digital ecosystem designed to give patients control over their data and unleash its potential for research and innovation.

The promise of test automation was simple: automate repetitive testing tasks, catch bugs faster, and ship quality software at scale. Yet for most development teams, that promise remains unfulfilled. Traditional test automation frameworks demand specialized coding skills, require constant maintenance when applications change, and create bottlenecks that slow down release cycles rather than accelerate them.

As enterprises race to deploy multicloud architectures and Agentic AI, they face a common bottleneck: "state." To govern AI token usage, manage agent-to-agent communication, or optimize performance via caching, API and AI gateways require a persistence layer to synchronize data. We’re excited to share the GA of Managed Redis cache for Kong Dedicated Cloud Gateways (DCGW).

A persistent challenge arises as businesses adopt multicloud architectures and agentic AI: the need for state synchronization. API and AI gateways require a robust persistence layer to synchronize data, whether it's for governing AI token usage, facilitating agent-to-agent communication, or boosting performance through caching.

As enterprises start deploying AI agents into real systems, a new architectural challenge is emerging. Agents need a reliable way to discover tools, services, and capabilities dynamically, instead of relying on hardcoded integrations. This is where the Model Context Protocol (MCP) ecosystem is rapidly evolving. MCP servers expose tools and capabilities that AI agents can use. However, once organizations begin deploying multiple MCP servers across environments, the question becomes clear.