Moving Analytic Workloads to the Cloud: A Transition Guide
Recent trends in information management see companies shifting their focus to, or entertaining a notion for a first-time use of, a cloud-based solution for their data warehouse and analytic environment. In the past, the only clear choice for most organizations has been on-premises data solutions —oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for some or all of a company’s analytical needs.
According to market research, through 2020, spending on cloud-based Big Data Analytics technology will grow 4.5x faster than spending for on-premises solutions. Due to the economics and functionality, use of the cloud should now be a given in most database selections. The factors driving data projects to the cloud are many.
Additionally, the multitudinous architectures made possible by hybrid cloud make the question no longer “Cloud, yes or no?” but “How much?” and “How can we get started?” This paper will reflect on the top decision points in determining what depth to move into the cloud and what you need to do in order to be successful in the move. This could be a move of an existing analytical workload or the move of the organization to the cloud for the first time. It’s “everything but the product selection.”
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