Systems | Development | Analytics | API | Testing

BigQuery autonomous embedding generation treats embeddings as a managed part of your table, making it easier to get your data AI-ready.

Use the Conversational Analytics API to build context-aware agents that understand natural language, query BigQuery data, and deliver answers in text, tables, and visual charts.

Your database contains thousands of answers. The problem isn't the data—it's that exploring it requires either technical skills or waiting for someone who has them. But what if you could sit down with Claude and just... talk through it? Ask a question. Follow a thread. Go deeper when something looks interesting. This isn't about running queries—it's about having analytical conversations with your own business data, guided by an AI that excels at exactly that kind of thinking.

User interface quality directly impacts whether customers stay, convert, or leave. That’s why UI testing tools have become essential for modern development teams. These tools automate user interface testing across browsers, devices, and screen sizes to make sure that applications look and work exactly as intended. UI testing tools help teams validate functionality, catch visual regressions, and detect cross-browser compatibility issues before they reach production.

With top-level Apache status, Polaris brings enterprise-grade governance and durability to the Iceberg catalog layer powering modern data platforms.

When designing on-premises and cloud systems, you have to balance resilience, security, and scalability. But ultimately, what your organization and business leaders care about is the bottom-line: today’s costs and tomorrow’s risk. As a result, hybrid and multicloud strategies are often viewed as simply a backup or disaster recovery strategy, instead of a path to availability your applications and business operations can really count on.

Organizations invest in IT infrastructure with one expectation: results, delivered fast. But when deployments drag on, the fallout goes far beyond slipping timelines. Slow rollouts can erode ROI, heighten operational risk, and strain already‑stretched IT teams. They also keep organizations from innovating at the speed today’s market demands—and the true cost of those delays often isn’t obvious until performance starts to suffer.

The right AI tools make all the difference in QA testing AI has officially entered every corner of software testifng. The hard part now is figuring out which tools and features actually save time, speed releases, and (most importantly) improve quality outcomes. According to a recent survey from Test Guild, 72.8% of testers are prioritizing AI-powered QA for 2026. This includes tools that handle test planning, test management, test writing, and even analyzing test results. Contents Toggle.