With ParStream, Cisco Thinks Edge Analytics


Cisco has made no secret that its intent to acquire ParStream is a play in edge analytics.

“This acquisition complements Cisco’s current data and analytics portfolio, improving our ability to provide analytics at the edge of the network, where data is increasingly being generated and in huge volume,” wrote Cisco’s Rob Salvagno in an Oct. 26 blog post. “The value of IT has always been derived from the intelligence contained in data.”

ParStream, a German company, specializes in analytics for the Internet of Things. As we’ve previously written, the ability to perform edge analytics for the IoT can make a huge difference. For example, by the time it takes data from a solar PV panel to reach a central office—which could be a minute—the data has lost much of its value in the market. A production flaw could also erupt and damage field equipment in a few seconds; for retail markets and supply-chain management, acting on remote, store-level data is crucial to survival.

Salvagno gave the example of analyzing real-time data on a wind power plant. ParStream’s technology can optimize wind turbine output in response to changing factors such as wind direction and temperature and also enables predictive maintenance. Without edge analytics, such data can lose its value as it lags moving to a central office for analysis. (For a ParStream use case, see “Value of Real-Time Data is Blowing in the Wind”).

“Now a company can store the data at the edge of the network, closer to the turbines and sensors, and track results even across a highly distributed network,” Salvagno wrote. “Real-time access to data derived from the connected equipment can lead to benefits like decreased equipment downtime through predictive maintenance, increased productivity, and historical analysis of environmental patterns.”

ParStream’s platform is powered by ParStream DB, which the company says can deliver sub-second query responses in analyzing billions of rows of data. The platform also includes tools such as geo-distributed analytics, alerts and actions, time series, advanced analytics, interfaces for streaming and ETL technologies, and allows integration of visualization tools.

Want more? Check out our most-read content:

Frontiers in Artificial Intelligence for the IoT: White Paper
Five Big Data Trends: Emerging Technologies
Why Machine Learning Is Crucial for Predictive Maintenance
Deciphering the Payments Dataverse in Real-Time

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Chris Raphael

About Chris Raphael

Chris Raphael (full bio) covers fast data technologies and business use cases for real-time analytics. Follow him on Twitter at raphaelc44.

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