Abstract: Big unstructured data analytics has become a hot IT trend with good reasons. There is actionable knowledge and insight that can be obtained from that unstructured data. Historically unstructured data (files or objects) did not fit well in traditional relational SQL database columns and rows. And that made it quite difficult to mine data to obtain that useful, actionable insight.

Unfortunately, big unstructured data analytics has several operational and financial obstacles that make obtaining those valuable, actionable insights and knowledge daunting. This paper will examine those obstacles in detail and explain how they can be effectively mitigated or eliminated utilizing Caringo Swarm.


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