Higher Performance, Scalability and Smarter Real-Time Insights are Powering Today’s Insight Driven Organizations

2018,  was a year of innovation, efficiency and agility as data generation continues to grow at unprecedented rates and the world simply becomes faster and indeed smarter.  The year proved that the need for speed and scale is escalating, and

Operationalizing Real-Time Machine Learning – A Financial Services Case Study

Operationalizing Real-Time Machine Learning – A Financial Services Case Study

One of my favorite films that depicted the power of Machine Learning (ML) for predictive analysis is Moneyball (2011). In the film, Brad Pitt who plays Billy Beane, the Manager of the Oakland A’s,  hires a Yale Economics graduate who

Real-Time Analytics Meets Kubernetes – Part 2: How to Auto-Deploy Your Machine Learning Stack

Real-Time Analytics Meets Kubernetes – Part 2:  How to Auto-Deploy Your Machine Learning Stack

In the first installment of our blog series on what happens when real-time analytics meets Kubernetes, we discussed how Kubernetes has become the de-facto open source containerized orchestration tool, as well as some practical tips for a one-click Kubernetes deployment

The Future of Financial Trading: Embracing Extreme Processing

The Future of Financial Trading: Embracing Extreme Processing

Drawing on the insights  shared by GigaSpaces, VP Product Yoav Einav as published in Computer Business Review, this article explores how in-memory computing meets the demands of applications requiring extreme high performance and low latency such as high-frequency trading.   There

InsightEdge Platform and XAP 14.0: Embracing Simplicity for Smarter, Faster Insights to Action

InsightEdge Platform and XAP 14.0: Embracing Simplicity for Smarter, Faster Insights to Action

I’m almost sure that when Da Vinci said “Simplicity is the ultimate sophistication,” he wasn’t speaking about real-time AI, machine learning and transactional processing. And yet his words are ever so relevant when we think about overcoming the complexities of