ParStream, the IoT analytics company, announced general availability of a new release of the ParStream Analytics Platform. Purpose-built to handle the volumes and velocity of IoT data, the new version includes the capability to perform edge analytics with ParStream’s Geo-Distributed Analytics (GDA) feature. GDA delivers a faster, more economical and secure way to analyze large volumes of data streaming from geographically disperse sources, such as cell phone towers, manufacturing operations, wind parks, oil platforms and supply chains.
ParStream’s GDA software can be installed at or close to the source of data, eliminating the need for a massive and expensive central database, enabling faster queries and real-time answers. Queries are executed centrally after which ParStream’s GDA transparently breaks up the query, sends them to the relevant remote servers, collects and delivers the results back to the application. What’s more, it lowers operational costs of data transfer and preserves network bandwidth for other purposes.
ParStream also announced EdgeAnalyticsBox, the industry’s first ruggedized appliance designed for edge analytics. EdgeAnalyticsBox comes pre-loaded with ParStream’s GDA software allowing data to be captured and analyzed at the source.
In addition to ParStream’s EdgeAnalyticsBox, customers can use their own hardware to implement ParStream’s edge analytics solution. ParStream’s light footprint allows ParStream GDA to be installed on small, standard-based servers or embedded in existing appliances already installed locally.
With a growing attention to what enterprise IoT customers are saying, ParStream has taken significant steps in developing its Geo-Distributed Analytics (GDA) platform features, now including the industry’s first ruggedized EdgeAnalyticsBox,” said Emil Berthelsen, principal analyst for Machina Research, a global advisor on the IoT, Big Data and M2M. “Designed as a hardware enablement tool of the ParStream IoT analytics software solution, the EdgeAnalyticsBox provides enterprises with a quicker time to market solution, allowing data to be captured and processed at the very source.”
ParStream also announced the results of a new independent, global survey focused on the untapped potential of IoT data. Survey findings link the access and analysis of data with the success of IoT projects. For example, only 8 percent of survey respondents are fully capturing and analyzing IoT data in a timely manner, while 86 percent report that faster and more flexible analytics would significantly increase the ROI of IoT Investments.
With more than two exabytes of data generated every day, data volumes are growing exponentially, arriving faster and coming from an increasing number of sensor-laden ‘things’ around the world,” said Peter M. Jensen, ParStream CEO. “In IoT, companies want to import massive amounts of data continuously, every second, every minute, every hour without affecting query performance. Traditional database and analytics technologies were not designed for that. The ParStream Analytics Platform is purpose-built for fast analysis of data while it’s still fresh, which has already proven a game-changer for many IoT use-cases.”
The new version of the ParStream Analytics Platform also includes IoT-ready API’s built for the speed and scale of IoT. The technology enables continuous high-bandwidth data import from every streaming data source. Connectors to a growing number of data sources are available at no cost as open source download under the Apache license.
Additionally, the new version enables ParStream’s customers and partners to run complex, advanced analytics algorithms as in-database functions. A large set of algorithms to derive more value from IoT data will be made available shortly.
Designed to enable businesses to more quickly analyze volumes of data, the analytics platform is powered by ParStream DB at its core. The company’s patented database has consistently been recognized for industry-leading performance with sub-second query response times in analyzing billions of rows of data. ParStream DB can analyze massive amounts of real-time streaming data as well as historical data.
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