Systems | Development | Analytics | API | Testing

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.

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.

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.

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

If you're already using an analytics tool today, here are seven questions that will reveal whether it’s still the right one, not just for insight, but for risk control and long-term scalability.

The preview of global queries in BigQuery lets you query data stored in different geographic locations with a single, standard SQL query, with no ETL.

The original promise of AI was that it would write most of the code for us. In reality, we’re not there yet. So where can AI meaningfully improve developer productivity today? In this post, we look at how AI powers development productivity across the SDLC, practical tools to use and frameworks for overcoming AI operationalization bottlenecks.

Over the past few weeks, an unusual experiment has been unfolding quietly and then very publicly.

Customers don’t buy software because they feel loved. They buy it because the product works, solves a real problem, meets security, scalability, and reliability requirements, and fits their budget. No amount of empathy or friendliness can compensate for missing features or poor performance. So at first glance, it’s easy to assume that great products alone win customer loyalty. But once the contract is signed and the product is in use, the rules change.

A global leader in entertainment, gaming, and hospitality partnered with WSO2 to take the organization’s first step to becoming an agentic enterprise by building an intelligent solution that would transform how support teams operate. The solution, a virtual support engineer, automated a manual issue resolution process and reduced resolution times from 2 hours to 1 minute, helping to provide a better support experience for both customers and internal operations teams alike.