Cloud computing allows for on-demand analysis, says
Cloud computing supports the iterative nature of big data analytics and allows data practitioners to focus on the data itself and how to really derive value from it.
He explains that data of any type can be loaded into the cloud, analysed as needed, and when complete, the cluster can be stopped and discarded, or additional data and capacity can be added depending on the outcome of the analysis.
However, he points out that if it so happens that an organisation has more technical specific requirements around its analysis, meaning that its computations will be memory intensive, the cluster can be provisioned accordingly.
According to Conradie, making use of cloud computing lowers the cost of entry, especially when it comes to big data analytics.
"Different variations of analytics services are available and can be used as needed for the analysis at hand to prove or disprove a specific hypothesis," he says. "If more system resources are needed, these can be added as required without going through a lengthy procurement process.
"This flexibility speeds up the analytical life cycle, which, in turn, saves time and reduces cost. Another benefit is that you don't need any specific technical skills to support and keep the system up and running, analysts can focus on the data and solving the business problem at hand."
He adds that cloud resources, in theory, are available immediately and there is no lead time required. "You don't have to plan now for future capacity while still in an exploration phase."
Conradie notes that these clusters for computing can also be started and stopped, where capacity can be increased or decreased, as demand dictates.
"Cloud architecture also alleviates the highly specific technical skills required to keep a big data system up and running – especially as these skills are rare and have a high 'price tag' associated and procuring them can be a time consuming process for any organisation."
Big data is still a fairly new concept to businesses and it's easy to get lost in the mass of technologies and solutions that are available, he warns.
"Focus continuously on the value that will be added, let the analysis support the value creation and then, lastly, do research on which technology will best support the analysis."
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