Datameer Introduces Version 5.0 of its Big Data Analytics Solution

Big analytics and visualization company Datameer is releasing version 5.0 of the company’s data analytics application for Hadoop, which adds improvements in speed, utilization, administration, user productivity, as well as a simplified tool set.

“Our vision overall is to make data simple and accessible for everyone,” said Matt Schumpert, director of product management with Datameer, about version 5.0 improvements.

The most significant upgrade for version 5.0 is the Smart Execution technology. The Smart Execution technology is basically a major upgrade of the applications engine. “It’s like going from a Volkswagen to a Ferrari,” said Schumpert.

All of the processes that can be performed by version 5.0 are possible as a result of the dynamic Smart Execution technology.  This technology is aimed at making the process of data analytics easier and more flexible for the user. The Smart Execution technology can take any size data and decide which compute framework can analyze the data most efficiently.

An improvement for version 5.0 is the storage capabilities. Traditionally, storage with Hadoop has been only with MapReduce. Version 5.0 now contains not only MapReduce and single node, but also in-memory storage. This allows for the best possible storage method depending on the quantity and type of data. The Smart Execution technology aims to analyze the data that comes through the bottleneck and then store it in the most efficient method possible for the consumer.

To view this, Datameer has also added transparency for the user. This allows for the consumer to view the data being processed by the Smart Execution technology. If the consumer feels they would like to make changes to the storage or processes, the transparency is a simple way to allow them to make those changes for their benefit.

Another new capability in version 5.0 is data flow programming concept, which provides the ability to run analysis using different types of compute frameworks and being able to adapt to different compute frameworks in the future. For example, as Spark becomes enterprise-ready, it will be able to be incorporated into the platform easier than before, explained Schumpert. This will allow for more flexibility for customers in giving them more choices.

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