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Multi-Structured Data Analytics

Hadapt was developed to eliminate data silos and to enable organizations to analyze massive amounts of "multi-structured" data in cloud environments using existing SQL tools.  Initially developed as research in the Yale Computer Science department, Hadapt's technology solves some of the critical problems of the big data analytic marketplace: an accelerating rate of data growth, an explosion of unstructured data, and a requirement to develop insights through the analysis of structured and unstructured data.

• Data volume & variety are expanding: The volume of data and its rate of generation are growing tremendously; it is no longer uncommon for an organization to have terabytes of data to analyze.  In addition to the steady growth of structured data, unstructured data is being captured at an ever-increasing rate.  Email, website log data, social networking data, customer feedback, files, and R&D info typically dwarfs the amount of relational data within a company, and those data sets hold significant and actionable insights - if they can be analyized.
• Hadoop-connected SQL databases don’t eliminate silos: Relational and unstructured data sets currently exist in silos. Relational SQL databases process structured data very efficiently, and Hadoop is similarly powerful for analysis of unstructured data. But neither system processes both - the current state-of-the-art is a parallel database with a Hadoop connector, which requires the transfer of large amounts of data between the two systems. Using these connectors can introduce delays, does not eliminate data silos, and increases TCO. 

Hadapt’s Adaptive Analytical Platform™ handles all data types and requires no connectors; it offers a complete solution to your data warehousing and analysis needs. With a scale-out architecture that can store and analyze petabytes of structured, unstructured, and semi-structured data, Hadapt provides organizations with a powerful platform for building multi-structured analytic applications.