Generating the initial workload recommendations after grouping accounts, applications, users, and profiles identified in those recommendations are preliminary steps toward optimizing the highest resource consumers on a system.
Although analysis can stop at this level, further optimization may be achieved by refining the recommended workloads. This represents the second level of analysis, in which additional who, what, and where parameters are chosen for further classification of the workloads. This additional level of classification enables identification of other similarities among workloads so that they will run even more efficiently.
This section provides information how to refine and analyze workloads at a basic level. Teradata WA also offers the ability to apply a recursive analysis of workloads with different correlation and distribution parameters. See “Using Deep Drill-Down and Refinement for Workload Analysis” on page 94 for more information.