Systems | Development | Analytics | API | Testing

The future of data and analytics will be nothing like the experience we're used to today. We are at the beginning of a transformation that will fundamentally reshape how businesses use data, make decisions, and create value. At the center of this revolution is Agentic AI. Agentic AI fundamentally changes the way we work with data – moving from passive, reactive AI systems to autonomous, goal-oriented agents capable of reasoning, planning, and executing complex tasks across diverse data landscapes.

Almost every web application is built for end-users to view and use. The application deployment process is an important operation in software development because it allows software to fulfill its purpose. This tutorial is primarily for software companies and developers to aid them in deploying and scaling web applications and services using a platform that automatically handles deployment with ease. Therefore, in this tutorial, we will be deploying a Django application to AWS.

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.

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.

Why do one in three data professionals cite integration challenges as the biggest roadblock to AI success? Our latest research uncovers the missing link to AI-ready data.

This article originally appeared on BigDataWire on Feb. 26, 2025. Artificial intelligence (AI) agents are set to transform enterprise operations with autonomous problem-solving, adaptive workflows, and scalability. But the real challenge isn’t building better models. Agents need access to data and tools as well as the ability to share information across systems, with their outputs available for use by multiple services—including other agents.

Android test automation is the process of using software tools and scripts to execute tests on Android applications automatically.

During our recent webinar Quality Horizon 2025, the virtual room buzzed with energy, filled with insightful questions that pushed our thinking forward. But one particular query truly struck a chord, a question that elegantly highlighted a core challenge in AI-driven testing: The observation was spot on. It perfectly captured a critical limitation we’ve seen across the current AI testing landscape.