One of the greatest challenges for data warehouse (DW) platforms and architectures today is that they must accommodate multiple, diverse workloads. Such diverse DW workloads support standard and specialty reports (dashboards, scorecards), basic analytics (OLAP), advanced analytics (mining, predictive), departmental analytics (data marts), and data staging (operational data stores (ODSs) for detailed source data or real-time data feeds), plus operational business intelligence and other real-time business practices. Sometimes, there is also a need to run OLTP workloads alongside DW workloads to support real-time data warehousing requirements.
The use of a private cloud as a consolidation platform addresses this challenge. A private cloud can accommodate multiple workloads better than traditional, distributed approaches. As DW workloads start up and shut down, the cloud provides generous processor and storage resources, to assure processing speed and volume scalability. And the cloud can recover and re-allocate these resources efficiently as workload processing ceases, or as temporary DW structures like data marts are no longer needed, . The cloud has similar advantages for business intelligence (BI) platforms, where the number of reports and concurrent users varies unpredictably.
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