What It Does

What It Does

BigPanda automatically correlates IT alerts into high-level incidents, helping you improve detection, accelerate remediation, and increase productivity.

• Improve detection: When IT teams have to deal with massive volumes of alerts, important incidents often fall through the cracks. By intelligently grouping and correlating alerts into unified incidents, critical issues are easier to find.

• Accelerate remediation: BigPanda’s provides the full context of an incident, instead of just a single data point. For example, instead of analyzing an isolated disk I/O alert, you could immediately see that an entire cluster is experiencing disk latency issues. Time to resolution is faster as fewer alerts are lost in the stream of noise.

• Boost productivity: Correlating alerts makes it easier to manage emergency situations by reducing the number of items you have to address.

How It Works

How It Works

Out-of-the-box integrations make it easy to connect your monitoring systems to our cloud-based platform.

• Quick setup: It takes just minutes to integrate your existing monitoring tools (such as Nagios, New Relic, AppDynamics, Splunk, Zabbix, Cloudwatch, Pingdom and more).

• Data normalization: BigPanda analyzes and normalizes alerts from all monitoring tools into a unified data model to capture and synthesize relevant information.

• Rapid time to value: No need to define rules or build a dependency model – BigPanda works automatically to provide results within minutes. However, you can always customize BigPanda’s correlation rules to suit your specific systems, applications, topologies, and division of duties.

Case In Point

Case In Point

BigPanda is valuable for resolving network, host-based, and application-level issues, along with hundreds of other use cases.

• Network issues: There are many types of network outages, but all are notorious for generating excessive noise. The crash of a single router can easily yield 500 alerts. BigPanda correlates all related alerts into a single incident.

• Host-based issues: Closely related alerts can represent dozens of metrics from a single host, including low memory, page faults, memory utilization, and high CPU loads. BigPanda groups alerts based on standard or customized logic to streamline troubleshooting and reduce noise.

• Application-level issues: There may be many hosts related to a single application; for example, a Java billing application that runs in a Tomcat application server spread among 50 hosts. BigPanda groups all alerts related to the same application, no matter which host or what data center the alerts arise from.

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