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IBM invests in IoT analytics with new 'Quarks' tool

22 Feb 2016

IBM has released a new technology that brings continuous streaming analytics to the open source community.

Quarks embeds streaming analytics onto Internet of Things (IoT) devices - and the company has submitted a proposal to Apache Software Foundation to request incubation of the technology.

Analysing data at the edge continuously can help companies generate insights more quickly and reduce network communication costs, according to IBM.

IDC predicts that the worldwide installed base of IoT endpoints will grow at a rate of 21.4% through 2019 to 25.6 billion endpoints with IDC expecting approximately 30 billion connections in 2020.

These devices will be enabled with digital sensing, computing and communications capabilities, giving passive objects a digital voice and the ability to create and deliver new data streams, IBM says.

Developers and data scientists can use the open source code in Quarks to build new apps that can handle massive amounts of IoT data streaming from sensors, smart metres, mobile communications and other connected devices.

Businesses across industries - from automotive and healthcare to telecommunications and manufacturing - can reduce communication costs and decrease time to insight with Quark's ability to deliver real-time analytics, boost application intelligence, and improve cognitive systems, IBM says.

"As businesses require more efficient analytics for the variety of connected devices they're using, Quarks can provide tremendous amounts of potential as a streaming analytics solution for the IoT. Its ability to integrate with a rich ecosystem of data sources, allows users to draw greater insight from more data with less work," says Nagui Halim, IBM fellow and director of IBM Streams.

"By contributing Quarks to the open source community, innovation will move faster, and can enable businesses to move from raw data to insight-driven actions more quickly,” Halim says.

Quarks was conceptualised for the open source community based on the high scalability and dynamic adaptability of IBM Streams. Many clients today are using IBM Streams as way to visualize data, expand the use of data analytics to a much broader base of users, and help build new products and services, IBM says.

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