Using Hive to Perform Advanced Analytics in Hadoop

Hadoop data warehouses have continued to gain popularity — with solutions such as Hive, Impala, and HAWQ now frequently deployed at customer sites. Access to these warehouses is typically tightly controlled using Ranger or Sentry — ensuring comprehensive data security. Due to the ease with which data can be governed in Hive, an increasing number of IT departments are locating all of their Hadoop data there and requiring data scientists to interact with their data only via Hive. Data residing in these warehouses is typically accessed via SQL. This is great for ETL and reporting, but it can be limiting when data scientists wish to leverage advanced analytics to train a random forest, for example. Fortunately, Chorus provides functionality that allows users to seamlessly leverage advanced analytics on Hive data…

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