The two companies have worked closely to build predictive models in areas such as healthcare, banking, telecom and publishing
Storage hardware applications provider EMC and cross-functional predictive analytics firm Alpine Data Labs have partnered to combine Alpine's web-based predictive analytics application with EMC Greenplum to offer a new database application for Big Data predictive analytics.
The integrated application combines Alpine's analytics software for data mining, modeling, and predictive analytics on Big Data with Greenplum's computational capabilities of parallel data processing.
Combining analytical processing features of Greenplum with Alpine's application will offer organisations a complete analytics infrastructure 'in a box,' to ease deployment and gain deeper insights on their Big Data, the company said. .
The two companies have worked closely to enable customers identify business trends and build predictive models in areas such as healthcare, banking, telecom and publishing.
The new combined application enables to perform analytics directly in the Greenplum Database and Greenplum HD to deliver critical benefits such as minimal data movement, built-in scalability, one-click deployment and an iterative approach to data modeling.
Alpine Data Labs has integrated Greenplum Chorus, the social toolset for Big Data, to leverage its predictive analytics capabilities into the collaboration application, enabling users to analyse Chorus datasets with predictive models, workflows and then publish the resulting insights for further collaboration.
Under a resell agreement, EMC will resell the Alpine Data Labs product collection like Alpine Illuminator, Alpine Miner, along with EMC Greenplum Chorus, EMC Greenplum Database, EMC Greenplum Data Computing Appliance (DCA) and EMC Greenplum HD.
Alpine Data Labs president and chief executive officer Tom Ryan said: "With the Greenplum engine powering Alpine's accessible user interface, we've made it easier for data scientists, engineers, business analysts, and executives to gain deeper, faster insights from their data, generated by sophisticated predictive analytics and data mining."
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