Big data, machine learning shape performance-monitoring developments

This report from 451 Research examines the requirements for performance-monitoring tools as modern applications have grown more complex, incorporating numerous data sources and containers services. Their report explores the key points of consideration for evaluating the new tools available in the IT operations analytics (ITOA) space, and includes profiles for a number of specific vendors that have been shaping the industry’s direction. The siloed model of yesterday’s ITOA tools, which analyzed resources separately, fails to capture the interconnectivity, breadth, and depth of today’s application environment. Vendors of performance monitoring tools and IT operations teams have taken a number of approaches attempting to solve this problem. Some have gone as far as rigging together combinations of third-party proprietary and open-source tools for homegrown monitoring platform. Most attempts, however, have proven too maintenance-heavy, costly, or unsophisticated to prove tenable. As such, a new breed of ITOA tools have entered the market, offering advanced analytics on data collected from practically any resource—apps, infrastructure, and even third-party monitoring programs. Longstanding industry veterans and new market entrants alike have been redefining the industry’s landscape with these tools. Points of differentiation between these tools involve their scalability, openness, machine learning capability, and analytics resources.