Quick answer: No, you don’t always need data in motion to operate in real time. However, some high volume/low latency real-time systems do use data in motion in the sense that data is processed before being stored.
To effectively tap into the transformative power of AI, telecom providers must undergo a fundamental shift in their thinking and adopt innovative business models geared towards driving growth.
In this week's real-time analytics news: MLCommons formed a new working group to produce machine learning benchmarks for client systems such as desktops, laptops, and workstations.
The benefits of Data Mesh transcend industry silos, democratizing data, breaking down barriers, and fostering collaboration and innovation.
A discussion on the importance of having trust in industrial data, the challenges that have prevented past technologies from working, and how Generative AI helps.
Make your transactions more profitable, more effective, and cheaper to handle by adding real-time decisions to your mainframe systems.
Unified Real-Time Platforms (URPs) are a new category of software designed to handle demanding applications that deal with both streaming data and data at rest. They combine elements of traditional Event Stream Processing (ESP) platforms and real-time data management, while providing additional application enablement features into a single, integrated system.
In this week's real-time analytics news: Databricks launched a unified data and AI platform for telecommunications carriers and network service providers.
Becoming an AI-first organization requires that businesses today get foundational digital transformation right.
Process industries face many obstacles that are preventing the straightforward deployment and adoption of Gen AI. Here are some tips to overcome those obstacles.