The battle over machine-generated data is heating up with the introduction of an important new analytic capability from Loggly Inc. that promises to help administrators work faster through the vast and growing amounts of information coming off their infrastructure. It’s designed to provide a more productive alternative to manually looking for specific records.
That has been made significantly easier by the emergence of high-level search options including the startup’s own cloud-based service over recent years that provide easier access to the data than traditional command line tools and come with visual filters for quickly finding particular subsets. But that granularity doesn’t extend as easily to specific metrics within the logs.
Loggly’s new feature aims to change that. The addition will provide the ability to defined so-called derived fields representing the specific parts of incoming records earmarked for analysis, which are then automatically extracted into an aggregation free of the clutter in the complete output stream. That acts to kills two birds with one stone.
Not only will administrators be able to find the information they’re looking for with much less work than currently needed, and thus faster, but also in a much more organized fashion that avoids the risk of key information getting overlooked that’s inherent to manual searching. The combined productivity benefit from those two factors grows with the size of the organization.
In large environments where the responsibility of keeping the infrastructure supporting business operations is divided among multiple teams, each can create summaries tailored for their specific areas of focus. Network administrators are thus able to narrow down the data displayed in their consoles to traffic statistics while security practitioners can likewise focus only on the subset relevant to their work.
The update puts Loggly in a stronger competitive position against arch-rival Splunk Inc., which dominant the log management space and has offered similar functionality for quite some time. But most importantly, support for derived feels will enable customers to react much faster to technical problems, malicious activity and other crucial trends hidden in machine-generated data.