As user organizations dive deeper into big data analytics, many are depending more heavily than ever on SQL-based ad hoc queries as their primary method for data exploration and discovery analytics (sometimes called investigative analytics). At the same time, the same organizations are adopting or considering Hadoop as their primary storage platform for big data. SQL-based analytics and Hadoop are good choices in isolation, but bringing them together has a catch: Hadoop’s support for queries is minimal at the moment.
A straightforward solution is to use a specialized analytic database management system (ADBMS) to query big data in Hadoop and elsewhere. This way, you get the rich features and query optimization capabilities of a mature ADBMS, along with the massive data store of Hadoop. And, compared to Hadoop, an ADBMS is far more conducive to the iterative approach to query development that most business analysts and data scientists demand for true investigative analytics.
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