How do you solve a performance problem when the cause is unknown, you don’t know where to start—and time is of the essence?
If you work in IT Operations, this is one of those troubling questions that keep you awake at night. It’s right up there with pesky intermittent performance problems that impact performance and availability. Because they come and go, they’re nearly impossible to catch, identify their root causes and fix them.
The answer to both scenarios lies in machine data. Today’s IT environments contain a wealth of information about system usage, performance, events, configuration changes, customer data, business metrics and so on. Increased efficiencies and capacities of modern storage systems have reduced the cost per record for data storage and made it financially practical to capture, store, and analyze more log data over longer periods of time.
However, while logs have played a role in troubleshooting and raw data analysis, the overwhelming growth in log data in recent years—and the unstructured nature of that data—has made it nearly impossible to find that crucial insight, “the needle in the haystack,” that can accelerate the remediation of the problem and prevent future problems. Analyzing massive volumes of logs can still be a very manual process, and traditional log search techniques are simply not keeping pace.
It’s little wonder that as potentially valuable a source as machine log data could be, most companies have not yet fully and effectively utilized it to troubleshoot performance problems.
New approach to log data analysis
Now a new white paper, “Analyzing machine data—the best way forward” describes a more effective approach to harvesting the insights that are hidden within log data. The approach is based on automating log analysis and applying sophisticated machine learning to the operations analysis process, so that you can pinpoint the root cause of performance issues in minutes rather than in hours or days.
The white paper introduces a more intelligent approach to log analysis and examines three critical aspects:
Group similar logs together to expedite processing
Use machine learning to find patterns and determine relevance of logs
Tune log analytics with SME expertise to optimize accuracy
Download the white paper to learn more about how you can extract highly relevant, actionable insights from your log data using automated, systematic analysis to quickly identify the root cause of performance issues.