The rapid growth of data and the changing nature of data applications is challenging established architectural concepts for how to store big data. Where once organizations may have first looked to large on-premise data lakes to centralize petabytes of less-structured data, they now are considering scale-out file and object storage systems that give them greater flexibility to store data in a way that meshes with the emerging multi-cloud and hybrid paradigm.
Since Hadoop’s hype bubble burst, enterprises have looked for other ways of storing the gobs of semi-structured and unstructured data that accounts for the bulk of the big data deluge. Enterprises want to use this data for a variety of use cases, not the least of which is training machine learning models to automate decision-making.
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