When running IT Analytics (ITA) cube processing jobs, the jobs take too long to complete compared to their historical or expected processing duration.

Cube processing includes the processing of both dimensions and measures. When multiple processing jobs sharing the same dimensions are run concurrently rather than as part of the same job, these dimensions will be reprocessed multiple times*, extending the length of time each processing job runs. In turn, multiple jobs running concurrently will have a more significant impact on system resources, resulting in general slowness across all jobs.

*This is also why when only one cube is processed, related cubes also show that they were processed at the same time even though they were not selected to be processed; e.g., DLP Incident Details and DLP Incident Summary.

To improve processing performance, consolidate processing jobs by the type of data they represent, or by the frequency with which they are processed. For example:

• Create a single job to process all DLP Discover cubes (DLP Discover Incident Details, DLP Discover Incident Summary, DLP Discover Scans)
• Create a single job to process all cubes from the same data source that are processed weekly or monthly v. those that are processed daily