The effective utilization of data might be one of the single biggest advantages of undergoing a digital transformation, but many companies struggle with the changes required to stream and process the enormous quantity of data at their fingertips. Digital transformation requires major changes to IT infrastructure to process this data, optimize business, and control actions in near real time.
Many times, these functions cannot be supported cost-effectively by current IT infrastructures. Data Warehouses and Big Data Clusters are expensive and slow. Organizations hope to move workloads to the Cloud to support the impact of digital transformation and build new applications faster, scale better and meet Service Level Goals (SLG) for existing and new production workloads with the lower cost.
The problem is how do you determine which workloads should be supported by an On Prem environment and which are good candidates for the Cloud? What is the appropriate Private, Public or Hybrid Cloud Platform for your environment? How much will it cost? You need an infrastructure that will support SLGs for each of the growing and future workloads with the lowest cost. You have to be certain that each workload is hosted in the right platform. The “Try and See” approach is too risky and estimating costs over time is difficult.
We will review three use cases that illustrate how benchmarks result. Modeling and optimization were used to evaluate the On Prem environment, Snowflake, RedShift and Vantage Clouds. Results of the benchmarks determine the change of the CPU and I/O service time for each of the workloads. Models determine the scalability and minimum configuration of the elastic Cloud that will be required to support SLGs for each of the existing, growing workloads and future workloads over the next 12 months. And, finally, the predicted total cost of running growing workloads on each of the optional platforms used to select the appropriate Cloud.
CEO and Big Data Expert,
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