Combines co-processing of structured and unstructured data with workflow productivity engine to boost collaboration among data science teams
EMC Corporation has introduced the EMC Greenplum Unified Analytics Platform (UAP), a platform to support big data analytics, that combines the co-processing of structured and unstructured data with a productivity engine that enables collaboration among data scientists. The new EMC Greenplum UAP brings together the EMC Greenplum database for structured data, the enterprise Hadoop offering EMC Greenplum HD for the analysis and processing of unstructured data, and EMC Greenplum Chorus, its new productivity engine for data science teams. Greenplum UAP will be available in the first quarter of calendar 2012.
According to Greenlum, the platform's approach to big data analytics leverages all of an organization's data-structured and unstructured-and provides tools to support the data scientist and other professionals who are increasingly part of the data science team. In order to glean insight from massive volumes of data and take the right actions, the company says, organizations need to do more than handle the co-processing of structured and unstructured data - they also need to ensure that the people who work with the data are able to collaborate and be as productive as possible, and Chorus meets that need by offering a single interface for all an organization's data together with virtual databases for exploration and innovation, and social collaboration for insight and analysis.
Greenplum has had versions of Chorus for the past 12 to 15 months and its own data scientists have been using it as they work with customers, but it has not been available for public use until now, Luke Lonergan, vice president and CTO, Greenplum/Data Computing Division at EMC, tells 5 Minute Briefing. So, while designated a "2.0" release, Lonergan says that this version of EMC Greenplum Chorus is actually the first commercial distribution, and is being launched to provide data science teams with a new way to collaborate across dispersed geographies and with very large data sets. Data scientists need solutions that enable them to share more easily the kinds of approaches and techniques they are using to gain data insights, says Lonergan. Through the Chorus interface, users get access to tools, data and supporting resources that enable enterprise-wide big data productivity, and rapid collaboration across data science teams helps to ensure useful insights get back to the business in time to take the right actions, thus increasing agility and innovation.