Kafka, Events, and the Future of Application Development (Webinar)

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.

Security must scale alongside AI development rather than lagging behind. This requires rethinking AppSec and risk management as a continuous practice driven by intelligence.

AI can improve industrial inventory management where traditional systems struggle most. This includes forecasting intermittent demand, positioning inventory across multiple sites, improving execution accuracy, and moving surplus inventory from planning to action. In each case, the value comes from better decisions grounded in data.

Enterprises have built intelligence layers that can observe, predict, and recommend, but they have stopped short of allowing AI to execute decisions end-to-end. Here is what’s needed to overcome this.

In this week’s real-time analytics news: Apache Software Foundation (ASF) announced that Apache HugeGraph has become a Top-Level Project (TLP).