Datameer Smart Execution and Datameer Version 5.0

Datameer Smart Execution and Datameer Version 5.0 : Datameer, announced the general availability of its breakthrough technology, Smart Execution and Datameer Version 5.0.

Millions of dollars have been invested in developing MapReduce code for large data analytics projects. With each new technology that joins the fray, code needs to be migrated in order to take full advantage of the proliferation of tools, a time-consuming, expensive and inefficient process. Today, a number of big data computation frameworks exist to handle a variety of workloads, and that number is expected to increase as work environments grow in complexity. Datameer, the end-to-end big data analytics application for the Hadoop ecosystem, is eliminating this complexity by introducing Smart Execution, a technology that examines dataset characteristics, analytics tasks and available system resources to intelligently and dynamically determine and engage the most appropriate execution framework for each workload. This selection is completely transparent to the end user and does not require IT assistance or extra hardware or software.

Beta customers have seen a performance increase of up to 2200 percent in individual analytics tasks and up to a 200 percent overall execution speed improvement for their big data analytics pipeline, while consuming far less cluster resources.

Datameer 5.0 with Smart Execution intelligently and dynamically selects the best-of-breed computation framework at each step in the big data analytics process to determine the most efficient option for the task at hand. Large data analyses will be executed in a Hadoop cluster using Apache Tez, an optimized form of MapReduce, while smaller data analyses will be executed on a single Hadoop node or using new in-memory technology. Moreover, this flexible architecture can easily incorporate new advances in the Hadoop ecosystem as they become available and are hardened to become enterprise-grade, including technologies such as Apache Spark.