Systems | Development | Analytics | API | Testing

In the evolving landscape of agentic systems, Confluent and Google Cloud together emerge as critical enablers, providing the real-time infrastructure that underpins efficient, reliable, and intelligent data flow. This powerful synergy addresses key challenges in agent-to-agent (A2A) communication, interaction with external resources, and the overall stability and observability of complex multi-agent environments.

The new StudioAssist Agent Mode turns your AI assistant in Katalon Studio into a connected, context-aware testing partner. It now supports MCP Servers, HTTP-based services that let the agent fetch real-time information and perform actions directly inside your project. Katalon ships with two built-in MCP Servers: You can also add your own HTTP-based MCP Servers to extend StudioAssist’s reach. (Note: authentication support is coming soon.)

Imagine an airline system monitoring traffic around an airport. If it detects a major delay, countless systems may need to react instantly: Ground operations to adjust flows. Some of these systems will still connect via API, traditional MQ or iPaaS technologies, but the data’s volume and urgency and the ease of decoupling apps make architecting with Kafka the better fit. The natural question is: should all these applications & systems connect to the same Kafka cluster?

Testing database-driven applications is notoriously painful. If your app depends on MySQL, you’ve probably spent hours setting up local databases, running migrations, loading data, and then cleaning everything up just to rerun your tests. This repetitive cycle slows development, breaks pipelines, and introduces inconsistency between local and production environments.

For the tenth consecutive time, Gartner has named Google Cloud Apigee a Leader in the 2025 Gartner Magic Quadrant for API Management.

Query templates in BigQuery data clean rooms let you create pre-defined queries that run against specific tables, reducing the risk of data exfiltration.

Federal agencies are right to prioritize commercial off-the-shelf (COTS) solutions for procurement modernization. Recent executive orders have provided a clear mandate for 'buy before build'—steering agencies toward proven, market-ready technology to accelerate mission delivery. But not all off-the-shelf procurement solutions are created equal. The technology landscape has evolved, revealing a critical divide in the world of off-the-shelf acquisition software.

Healthcare organizations are under growing pressure to connect legacy EHR (Electronic Health Record) and ERP (Enterprise Resource Planning) systems while safeguarding patient privacy and meeting strict compliance standards. Most of these systems — Epic, Cerner, MEDITECH, Infor, Oracle, SAP, and others — rely on enterprise-grade databases like Oracle, SQL Server, IBM DB2, SAP HANA, InterSystems IRIS, and PostgreSQL.

In Europe, trust is everything, and the bar is set by law. GDPR, the AI Act, NIS2, DORA, and the Data Act shape how data and AI must operate. Leaders need to show where data lives, who can touch it, and how it moves, and they want cloud speed and flexibility without giving up control, so sovereignty and transparency must be built in from day one.

In part one of this series, we walked through how to use Showcase in a Rails app. It's now time to read some Ruby code written by experienced Rails developers. To do this without getting lost, we'll choose one feature of the showcase engine and analyze how it works: rendering a preview of a component. Let's get started!