Today, ParStream reveals details of an analytics platform dedicated to the internet of things.
Featuring ParStream DB, a patented database designed to handle the storage of massive amounts of data and real-time analytics, the ParStream platform boasts sub-second query response times, the ability to analyze billions of rows of data and time series analytics that analyze incoming, real-time data feeds in conjunction with historical data. Moreover, ParStream’s Geo-Distributed analytics server enables data analytics and data visualization on distributed datasets in ways that accommodate the disparate data storage infrastructures of contemporary global IT organizations. ParStream’s bevy of analytic offerings sit atop the ParStream DB as illustrated below:
As the graphic illustrates, ParStream’s internet of things analytics platform delivers a diverse range of analytic engines that operate upon massive volumes of data resulting from appliances, wearable devices, automobiles and sensors. Moreover, the platform boasts data acquisition qua ETL/streaming technologies and data visualization technology that collectively delivers a turnkey solution for internet of things use cases. As told to Cloud Computing Today in an interview with ParStream’s CEO, Peter Jensen, the ParStream solution absolves customers of the necessity of cobbling together disparate technologies that often struggle to integrate with one another by delivering instead a holistic solution for internet of things analytics that has few counterparts in the industry to date. The conjunction of the platform’s database and advanced analytics technologies renders it especially well suited for the massive, high velocity datasets specific to the internet of things space, particularly given its real-time analytic functionality on streaming datasets and capability to change the direction of its analytics as data evolves. More generally, the platform illustrates the Big Data industry’s increasing preoccupation with real time analytics on massive, streaming datasets and the concomitant challenges associated with storing incoming data and the results of analytic operations on the data in question.