Systems | Development | Analytics | API | Testing

For decades, testing was about ensuring predictable outcomes in mostly static environments. Today, AI-driven applications are dynamic, adaptive, and sometimes unpredictable. That raises the stakes for quality engineering. When Gartner rolls out a brand-new Magic Quadrant, it’s a big deal to those of us in the software industry. We believe it signifies a new, innovative tech has officially arrived on the global stage.

The launch of Nigeria's open banking era is a mandate that is rapidly moving from a regulatory concept to a technical requirement. For CIOs and CISOs of Nigerian banks, this shift presents both a significant compliance challenge and a strategic opportunity. The primary concerns are maintaining security while exposing customer data, understanding the specific technology needed, and ensuring the investment delivers ROI are paramount.

Looking to embed analytics into your software? Your method of integration boils down to a key trade-off between cost, time, and customization. Book a demo and see how Yellowfin makes embedding analytics easy. As a software product owner, you already know that embedded analytics is a game-changer for delivering a great user experience and creating a truly data-driven user experience.

On September 23 and 24, we hosted AI Engineer Paris 2025 at Station F — a one-day gathering of builders, researchers, and practitioners exploring the future of applied AI. With five talk tracks, 48 sessions, and 25 sponsors, the event brought together the best of the AI engineering community in Europe and worldwide. If you want a full recap of the key themes and takeaways from the talks, check out our event recap blog post.

In today’s data-driven world, companies depend on accurate and trustworthy data to make better decisions. ETL (Extract, Transform, Load) testing is a key factor in data reliability, as it ensures that data is accurately extracted from multiple locations, transformed correctly, and loaded into the target system correctly without loss or error.

‍ In 2025, enterprises are under more pressure than ever to do more with less, be agile, maintain reliability, and support growing adoption of AI, cloud, and hybrid systems. IT infrastructure automation is no longer a “nice-to-have” but is becoming mission-critical. Recent industry research shows just how fast this shift is happening.

Looking to integrate industrial data with modern web applications? Here's the gist: Kepware collects OPC UA data from industrial devices, while Ignition processes and shares it as REST APIs. Together, they bridge industrial systems with web tools like dashboards, mobile apps, and cloud analytics. Key Takeaways: 1. Kepware acts as an OPC UA server, gathering and converting data from industrial devices. 2.

Two data challenges exist across almost all organizations: access and trust. These issues scale exponentially as an organization grows to the point that it can no longer hand around sheets of paper or approve database access. The demand for better data access drove the history of data warehousing, following the ethos that better decisions come from more data and that compute would catch up with demand. However, the hunger for collecting more data didn’t come without a cost.

As enterprises embed gen AI into their workflows, many are discovering a minefield of risks. Data privacy breaches, misinformation, adversarial attacks and hidden bias are just a few of the challenges that can derail gen AI initiatives. These aren't just technical concerns, they're business-critical issues that can erode trust, trigger legal consequences, and tarnish reputations.

ClearML Enterprise v3.27 delivers on the three capabilities most requested by practitioners : clear visibility into compute consumption inside projects, simpler and safer access control for remote sessions and deployed endpoints, and quality-of-life upgrades across the UI. The result is better cost control, stronger governance, and faster day-to-day execution.