Systems | Development | Analytics | API | Testing

As the demand for data to power AI models and real-time decision making continues to grow, organizations are increasingly looking for ways to simplify and optimize the ways to ingest, and process fresh data within the enterprise. On average, organizations allocate 20–50% of their annual data warehouse spend on compute for data ingestion, amounting to millions of dollars in costs for large enterprises.

In the AI era – the need for enterprise-wide data—from every operational system—available for instant analytics is real. Microsoft Fabric has fundamentally simplified the modern data estate, centering everything around the flexible, unified power of OneLake. A cornerstone of this platform is Mirroring, a low-latency, low-cost solution designed to break down silos. But for data engineering pioneers, the most exciting development is Open Mirroring.

The Kubernetes networking landscape is evolving, and recent announcements confirm a significant community shift. The Kubernetes community recently announced the upcoming retirement of one of its most widely used components: Ingress NGINX "To prioritize the safety and security of the ecosystem, Kubernetes SIG Network and the Security Response Committee are announcing the upcoming retirement of Ingress NGINX. Best-effort maintenance will continue until March 2026.

Is your artificial intelligence (AI) underperforming? You're not alone. Stale, fragmented data is a leading obstacle to optimal AI performance. Outdated information cripples your decision-making, fuels operational inefficiencies, and leads to missed opportunities—from slow fraud detection to irrelevant chatbot responses. AI’s rapid evolution makes real-time data even more critical: That’s why real-time data streaming isn’t merely an option.

Software teams today are routinely under pressure to release features more quickly, while keeping quality in check, in today’s fast-paced digital ecosystem. Automation testing enables teams to develop this balance; however, most teams find that writing and maintaining test scripts becomes a heavy burden with technical complexity, and takes time away from building features. This is where a Test Recorder is of great assistance.

In a previous blog post, we introduced one of the most significant advancements in Qlik Predict to date — multivariate time series (MVTS) forecasting, bringing enterprise-scale accuracy and context to complex prediction problems. But MVTS is only part of the story. Over the past few months, we’ve continued to enhance Qlik Predict with several powerful updates designed to make predictive AI more trustworthy, fair, and connected for our customers.