Santa Clara-based machine data analytics vendor Glassbeam recently revealed details of a new version of Glassbeam SCALAR marked by deep integration with Apache Spark. Apache Spark is a parallel data processing framework that facilitates real-time analytics, machine learning and real-time analytics by storing the results of data operators in memory and performing low latency, iterative calculations on in memory computational results. Known for its ability to automate the parallelization of tasks and jobs, Spark boasts operational efficiencies over MapReduce by a factor of 100 with respect to the execution of calculations on large datasets. Glassbeam SCALAR’s integration with Apache Spark enhances its computational capabilities as well as the platform’s machine learning functionality and capacity to perform real-time analytics on streaming datasets by means of the Spark Streaming and MLLib components of the Spark stack. Built on Cassandra, Spark’s addition to the Glassbeam’s cloud analytics platform gives it the benefits of Cassandra’s distributed data management architecture in addition to Spark’s computational, analytic and machine learning functionality. As such, today’s announcement strengthens Glassbeam’s position in the nascent but exploding internet of things analytics space by augmenting its ability to ingest, process and analyze massive amounts of data as well as enhancing Glassbeam SCALAR’s advanced analytics, machine learning and predictive analytics capabilities.
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