Systems | Development | Analytics | API | Testing

Enterprise Cluster Autoscaling, Private Networking and Reduced TCO

ABOUT CONFLUENT Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations.

New: Connect Databox to Claude, ChatGPT, N8N, and more!

Most teams today are expected to move faster and be data-driven, but getting clear answers about performance is still harder than it should be. Even simple questions often require jumping between dashboards, piecing together insights manually, or relying on a small group of data experts to dig in. The process can be slow, and it often leads to more questions than answers.

Why Enterprise AI Projects Fail - The Token Predictor Problem Executives Don't Understand

Why do large language models hallucinate? It's not a modeling problem. It's a data and context problem. This video breaks down why AI fails in enterprise environments and what it takes to get reliable, verifiable answers from your AI systems. When AI doesn't have governed access to live data, no understanding of your business rules, and no guardrails to keep it grounded, hallucinations aren't just likely. They're inevitable.

Model Context Protocol (MCP) Security: How to Restrict Tool Access Using AI Gateways

For too long, the Model Context Protocol (MCP) has operated on a principle of open access: connect an AI agent to an MCP server, and it gets access to every single tool that server offers. While this approach is simple for initial experimentation, it quickly becomes a liability in production.

How to Cut BI Ticket Backlogs with AI-ETL for Self-Serve Analysts

Your BI team didn't sign up to spend 69% of their time on repetitive data preparation tasks. Yet this is the reality for most data teams drowning in support ticket backlogs while strategic initiatives languish. Every hour spent manually updating schemas, troubleshooting failed data loads, or running ad-hoc queries is an hour not spent on the analytics that actually drive business decisions.

The new era of Healthcare Modernization in 2025 & beyond

Is your legacy healthcare system holding you back? Would you still wear a suit that no longer fits, just because it once looked great? Probably not. The same logic applies to your IT infrastructure. Healthcare organizations often grow comfortable with legacy systems simply because they’ve always worked. But what once worked well may now be putting your operations, patients, and reputation at serious risk.

Best Automated Mobile Testing Tools in 2026 (Top 10 Compared)

When choosing a mobile testing tool, consider: It's about choosing the mobile testing tool that fits. If you're still in consideration stage, we've got you covered. Here is a list of the best automated mobile testing tools and frameworks out there for you to try, with pros and cons listed to help you make informed decisions. Smart Summary Navigating the landscape of automated mobile testing tools requires aligning capabilities with team expertise and project requirements.

Top 5 AI-Powered SAST Tools for 2026

Static Application Security Testing has survived multiple cycles of skepticism, reinvention, and disappointment. For years, SAST was criticized for producing noise, slowing development, and failing to reflect real-world risk. Yet in 2026, SAST has not disappeared. It has changed its role. The shift is not that static analysis suddenly became perfect. It is that organizations finally stopped asking SAST to do the wrong job.

Why transparent AI is the only AI you can trust in QA

AI fosters speed. Transparency fosters confidence. AI for QA testing is suddenly everywhere. Every tool claims it’s “AI-powered.” Every demo promises smarter test generation, faster maintenance, and fewer bugs. Plus, with AI accelerating the pace at which developers write and ship code, QA leaders are under growing pressure to keep up. It makes sense that teams are looking for AI for QA testing. But here’s the uncomfortable truth: AI in QA only works if you can trust it.