Systems | Development | Analytics | API | Testing

The hype for Anthropic’s Model Context Protocol (MCP) has reached a boiling point. Everyone (including Kong) is releasing something around MCP to ensure they aren't seen as falling behind in the ever-changing AI landscape. However, in this mad dash, there remains confusion around MCP and what this standard actually enables. Some see MCP as a total game-changer, and some see it as little more than a thin and unnecessary wrapper. As usual, the truth lies somewhere in between.

Automated testing is status quo for a majority of software delivery teams today, yet two-thirds of teams say they deploy code without completing all the necessary testing – and that it costs them anywhere between $500,000 and $5M USD annually. That’s according to a recent survey Tricentis commissioned with Censuswide.

In an era where big data is often referred to as the “new oil,” extracting value from raw information is more critical than ever. However, this process is far from straightforward. Organizations today deal with data sprawled across SaaS platforms, on-prem systems, databases, CRMs, and countless APIs. Making sense of it requires powerful and reliable Extract, Transform, Load (ETL) capabilities — and that's where ETL consulting services become indispensable.

In 2025, the integration of Artificial Intelligence (AI) into Extract, Transform, Load (ETL) processes is transforming the data engineering landscape. Traditional ETL workflows are evolving from rigid, manually scripted pipelines into intelligent, adaptable systems powered by AI. These AI-driven ETL tools enable companies to handle increasing data complexity, schema drift, and real-time transformation demands without massive engineering overhead.

Data is the new oil—but without a well-structured refinery, even the most abundant data becomes noise. Data warehouse modeling is that refinery: the critical blueprint that ensures organizations can store, retrieve, and analyze data with precision and efficiency. As of 2025, the landscape of data warehousing continues to evolve rapidly.

Tracking API performance is essential to ensure smooth operations, enhance user experience, and prevent costly downtime. Here’s how to do it effectively.

When it comes to error monitoring in mobile apps, iOS and Android take distinct approaches due to their unique architectures and tools. Here’s a quick breakdown of the key differences.

Smarter AI starts long before the model is trained. It begins with the quality of the data you feed it. Data that reflects real-world nuance, cultural context, and human behavior is what sets strong systems apart.

The need for impeccable mobile applications is unequivocal. Users want intuitive interfaces, smooth functionality, and uniform performance across various devices and operating systems. Development teams have a considerable difficulty in satisfying these requirements while expediting release cycles. Conventional mobile app testing services, although fundamental, often fail to keep pace with the velocity and complexity of contemporary application development.

Agentic AI has the potential to revolutionize workplace processes in nearly every sector by improving business decision-making, workflow efficiency and customer interactions and experiences. While interest in agentic AI is widespread, the motivation to use it differs by industry. Cloudera surveyed 1,484 enterprise IT leaders across 14 countries to better understand their approach to agentic AI in 2025, including how specific industries plan to implement the technology.