Salvatore Salamone

By processing data locally, organizations can filter and act on the most important insights immediately, while sending only relevant or aggregated data upstream to the cloud for longer-term analysis and storage.

Orchestrating a robotic intervention, flagging an anomaly, or executing a stop command on the production line requires a real-time, intelligent edge.

In this week’s real-time analytics news: HPE and Dell Technologies bolster their offerings for AI workloads.

Industrial organizations that adopt DataOps principles early can accelerate AI deployment, reduce costly integration delays, and establish a sustainable foundation for innovation.

To mitigate AI agent sprawl problems, industrial organizations must integrate discovery, governance, monitoring, cost control, and orchestration into a unified operational framework.

In this week’s real-time analytics news: MLCommons announced results for its industry-standard MLPerf Storage v2.0 benchmark suite.

In 2025, predictive analytics is being widely applied across industries to anticipate events, optimize operations, and personalize experiences in real time.

In this week’s real-time analytics news: The Linux Foundation welcomed the AGNTCY project, an open-source infrastructure that enables discovery, identity, messaging, and observability among AI agents from different vendors and frameworks.

In this week’s real-time analytics news: AWS announced the open source release of Spark History Server MCP, an MCP server that enables AI assistants to access and analyze an organization’s existing Spark History Server data through natural language interactions.

Industrial AI won’t transform an organization’s operations unless its data is organized, understood, and trusted.

In this week’s real-time analytics news: Multiple companies make their solutions available in the new AI Agents and Tools category of AWS Marketplace.

Neoclouds are new-generation of AI-focused cloud infrastructure providers. They differentiate themselves from traditional hyperscalers (such as AWS, Azure, and GCP) by focusing on optimized GPU availability, flexible pricing, and specialized performance for AI/ML workloads, among other key features.