Log and event analysis demands large volumes of data to identify trends and patterns over time.
Traditional log and event management solutions provide cost efficient access and alerts on relatively small amounts of real-time data. But, as data volumes increase to hundreds of terabytes over multiple years, these solutions becomes extremely cost prohibitive. Typically data that is more than 10 days old is discarded or at best archived. Abandoning such valuable business data eliminates access to business insights that new machine-learning based technology can uncover.
- Based on long term analysis, identify bottlenecks or under-utilized IT resources.
- Understand trends and hot spots to improve infrastructure capacity planning and cost.
- Create reporting or set-up monitoring to visualize infrastructure health.
- Integrate operational intelligence to drive on-demand IT provisioning and configuration.
- See historical data across your entire IT infrastructure to identify patterns and long-term trends.
- Analyze years of data including system and applications metrics.
- Detect and monitor suspicious behavior and identify security threats.
- Predict future failures based on patterns associated with past anomalies.
- Leverage supervised learning to automatically search all of your logs and events, making correlations and finding interdependencies. Easily identify when a metric value or event rate deviates from its normal behavior.
- Leverage unsupervised learning to uncover and mine the unknown value of large volumes of log and event data. Detect anomalies that short-term datasets cannot uncover.
Instantly apply machine learning algorithms and analysis to multiple years of log and event data.Request Trial