Application server data
Application server data refers to the operational, configuration, and performance information generated, collected, or maintained by the servers that host and run software applications. This data supports the management, monitoring, troubleshooting, and optimization of application server environments, but does not include the business or user data processed by the applications themselves.
Key characteristics of application server data are that it is:
- Infrastructure-related: Pertains to the server, middleware, and hosting environment, rather than end-user or business process data.
- Operational focus: Captures details about the functioning and status of the server environment.
- Supports monitoring and management: Used for health checks, performance tuning, and issue resolution.
This data might include logs, configurations, and metrics.
Application server data typically includes:
- Configuration files: Port settings, connection pools, environment variables
- Deployment and startup records: Application version, deployment time, status
- Error and exception reports: Stack traces, exception messages
- Performance metrics: CPU/memory usage, thread counts, response times
- Resource utilization reports: Heap/disk usage, active session counts
- Security and access logs: Login attempts, authentication failures
- Server log files: Startup logs, error logs, access logs
You might also be interested in application data.
Add-ons and apps
Splunk Lantern articles for the Splunk platform
- Detecting malicious activities with Sigma rules
- Finding Windows audit log tampering
- Integrating Gigamon Deep Observability Pipeline with the Splunk platform
- Investigating unusual file system queries
- Monitoring log volume trends
- Monitoring web application performance
- Securing a work-from-home organization
- Using stack traces to detect application errors
Splunk Lantern articles for Splunk security products
Splunk Lantern articles for Splunk observability products
- Building a self-serve and scalable observability practice
- Getting Kubernetes log data into Splunk Cloud Platform with OpenTelemetry
- Getting traces into Splunk APM
- Implementing real-time cloud application threat detection with Secure Application and Enterprise Security
- Implementing distributed tracing
- Monitoring API transactions
- Monitoring applications using OpenAI API and GPT models with OpenTelemetry and Splunk APM
- Monitoring third-party API calls using the OpenTelemetry spanmetrics connector
- Speeding up root cause analysis with artificial intelligence
- Troubleshooting application issues (Splunk Observability Cloud)
- Troubleshooting critical application performance issues (Splunk AppDynamics)
- Using Azure DevOps integrations for events and alerting


