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As more organizations embed data science into their decision making, they are also moving to the cloud to support their efforts. In fact, TDWI research indicates that platforms such as cloud data warehouses or data lakes are a growth area for data management to support data science. The cloud has numerous benefits for advanced analytics; two of the top benefits are scalability and elasticity.
When you need to perform analytics processing on a large data set and iterate on that analysis, the cloud enables you to procure as much storage and compute services as necessary. When you are finished with the analysis, you are no longer responsible for those additional services. This is critical for analytics and especially for data science initiatives, which can be compute intensive.
However, moving data science to the cloud is about more than just elasticity and scalability; there are a number of reasons why the cloud makes sense for data science. There are also considerations for utilizing the cloud for more advanced analytics such as machine learning and AI. These include evaluating use cases to run in the cloud, cloud computing architectures, and planning considerations.
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