Incoming integrations
Incoming integrations enable IBM Cloud Pak for AIOps to collect log, metric, and event data from various sources.
IBM Cloud Pak for AIOps uses this collected data to establish baseline models during AI model training. These models are then used to compare against live incoming data from these integrations to report anomalous activity to you in near real time by way of your outgoing integrations. In live data mode, the integration might need a delay to offset the query time windows to provide time buffer to prevent retrieving partial real-time data. For more information, see Delay configuration in data integrations.
The recommended value for source parallelism should be equal to the number of days. For example, if you are pulling historical data for training for 7 days, the source parallelism should be around 7 or higher, so that you can pull data for multiple days simultaneously.
Unlike outgoing integrations, you can set up multiple sources of incoming integrations. For example, if you have a single source of Falcon LogScale data for logs, and PagerDuty configured for events, you can create integrations for both of those. You can also use the custom and Kafka integrator for greater flexibility. The custom integration type collects data from a single specific data source (like any other integration type). If you want to collect log data from different systems, and then route that information through a forwarding agent, such as an Apache Kafka topic (like Sysdig or FluentBit), you can use the Kafka integration type.
Note: The ServiceNow integration type has optional incoming and outgoing facets.
For more information about incoming integrations, see the following topics:
- Administrator process integrations
- Appdynamics integrations
- AWS CloudWatch integrations
- Custom integrations
- Dynatrace integrations
- ELK integrations
- Falcon LogScale integrations
- Generic webhook integrations
- IBM Netcool Operations Insight ObjectServer integrations
- IBM Tivoli Netcool/Impact integrations
- Instana integrations
- Kafka integrations
- Mezmo integrations
- New Relic integrations
- Observer integrations
- PagerDuty integrations
- Splunk integrations
- Zabbix integrations