7 Essential Elements in a Real-Time Streaming Analytics Platform

Companies that develop or support products in the field need to transition from reactive to predictive maintenance and service. Such a transition is possible using ubiquitous connectivity and predictive analytics on machine data. Here’s how.

Although edge architectures differ fundamentally from conventional cloud and on-premises environments, edge isn’t an alternative to traditional infrastructure strategies as much as it is an augmentation of them.

The relationships banks have with their customers can last for decades, but too many banks fail to use the data they have to provide personalized digital banking services. 

Every transaction, order, customer interaction, and internal operation depends on decisions. At a time when businesses are under pressure to improve efficiencies, cut costs, and be more responsive, automating decisions becomes essential. While many businesses have picked off the low-hanging fruit when automating, most need to add more. Specifically, businesses want to automate complex decisions, involving multiple steps, multiple data sources and many rules.

To operationalize sub-second RAG, the author proposes the FLASH Framework (Fast Latency Architecture for Streaming Hybrid retrieval), a 4-step methodology distilled from deploying 50+ enterprise pipelines since 2024.

Far from accelerating Excel’s extinction, AI agents are extending Excel’s lifespan by making it more accessible and more powerful.

In this week’s real-time analytics news: The EuroHPC Joint Undertaking focuses on AI Gigafactories and sovereign AI.

Organizations that embrace event-driven architecture early will build more resilient, scalable, and maintainable AI systems. EDA enables greater resilience, scalability, and observability, which are essential for robust, enterprise-ready Agentic AI.