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Adaptivity Blueprint*: Website Analytic Tools

This Blueprint is for a deployment of a Website Analytic Tool incorporating aspects of Business Intelligence (BI), based on a Red Hat* Enterprise Virtualization Hypervisor solution that leverages a Policy-Based Power Management Strategy to right-size the environment in correlation to its load. In addition, there are several design factors that predicate an understanding of the patterns of the Business Intelligence workload in question and how that workload behaves. The most significant patterns are called out for this application and are listed here by family, pattern name, a description, what problem the pattern solves (problem), key design decisions that influence the use of this pattern (driving forces), the typical participant patterns that this architectural pattern will use to solve the problem suggested by the scenario (collaborators), aspects of design than can be varied as a result of using this pattern (aspects that can vary), and the tradeoffs and results of using the pattern in terms of its limitations and constraints (tradeoff & constraints).

Using this knowledge, these Blueprint sheets were generated by first considering the size of the workload to be applied. Then performance requirements were used to generate virtual and logical views of the architectural, management, and physical infrastructure components needed to deploy this application in the cloud.

Instead of relying on the isolated intuition of architects and engineers to design the solution for cloud enablement, these blueprints are provided to ensure a more accurate and precise design is used as an initial instantiation to save on design, pilot, and ultimately rebuild costs, and to enable more rapid go to market.

Read the full Adaptivity Blueprint* Website Analytic Tools Brief.

Adaptivity Blueprint*: Website Analytic Tools

This Blueprint is for a deployment of a Website Analytic Tool incorporating aspects of Business Intelligence (BI), based on a Red Hat* Enterprise Virtualization Hypervisor solution that leverages a Policy-Based Power Management Strategy to right-size the environment in correlation to its load. In addition, there are several design factors that predicate an understanding of the patterns of the Business Intelligence workload in question and how that workload behaves. The most significant patterns are called out for this application and are listed here by family, pattern name, a description, what problem the pattern solves (problem), key design decisions that influence the use of this pattern (driving forces), the typical participant patterns that this architectural pattern will use to solve the problem suggested by the scenario (collaborators), aspects of design than can be varied as a result of using this pattern (aspects that can vary), and the tradeoffs and results of using the pattern in terms of its limitations and constraints (tradeoff & constraints).

Using this knowledge, these Blueprint sheets were generated by first considering the size of the workload to be applied. Then performance requirements were used to generate virtual and logical views of the architectural, management, and physical infrastructure components needed to deploy this application in the cloud.

Instead of relying on the isolated intuition of architects and engineers to design the solution for cloud enablement, these blueprints are provided to ensure a more accurate and precise design is used as an initial instantiation to save on design, pilot, and ultimately rebuild costs, and to enable more rapid go to market.

Read the full Adaptivity Blueprint* Website Analytic Tools Brief.

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