Data types in Splunk Observability Cloud 🔗
Splunk Observability Cloud provides you with the tools to collect, manage, and visualize the following data types: metrics, events, logs, and traces.
With Splunk Observability Cloud’s features, you’ll be able to build charts and dashboards, and set up alerts and other system notification methods. This will help you better understand the performance of your systems and services, detect anomalies, or plan deployments and enhancements.
Use Splunk Observability Cloud search to quickly locate the service, traceID, dashboard, chart, or metrics-based content you are interested in. For details, see Search in Splunk Observability Cloud.
Quick overview of Splunk Observability Cloud data types 🔗
Splunk Observability Cloud works with the following data types:
Metrics: A metric is a measurement about a data source (host, application) that varies over time. For more information, read Metrics in Splunk Observability Cloud.
Metrics and their metadata are stored in data points, which are then collected in metric time series.
Metadata includes dimensions, custom properties, tags, and attributes.
Metrics can be collected by Infrastructure, APM (as MetricSets), RUM, Browser, or Synthetics.
Splunk Observability Cloud also produces its own org metrics to help you understand how the platform is performing.
Events: Context added to metric data. See more at Add context to metrics using events.
Traces and spans: A collection of operations, known as spans, that represent a unique transaction an application handles. See more at Manage services, spans, and traces in Splunk APM.
Logs: Automatic, time-stamped record of a relevant event or activity. Log ingestion is configured for each feature. To learn how to query logs, see Introduction to Splunk Log Observer Connect.
Next steps: Tools and analytics 🔗
Splunk Observability Cloud provides a wide array of features and tools to help you manage, understand, and leverage your data. For more details, see Data tools in Splunk Observability Cloud.
For advanced analytics, use SignalFlow to analyze incoming data and write custom chart and detector analytics. See more at SignalFlow and analytics.