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February 22, 2019

Streaming Data App Builder Goes Open Source

(artida/Shutterstock), promoters of an agile approach to data collection and analysis either in the cloud or at the network edge, has released an open-source version of its platform for building real-time, distributed applications. said its open source platform provides an integrated software stack that enables developers to build applications that analyze, learn and act on streaming data as its generated either locally by edge devices or in the cloud.

The San Jose-based company founded in 2015 likens its edge-based software to a distributed operating system for building “real-time analytics and machine learning applications.” Company executives dismiss centralized “store then analyze” architectures as unfit for edge applications like the Internet of Things (IoT).

The company’s stateful model in which data is saved and passed along is able to run from small edge devices “up to the largest clouds,” said Thursday (Feb. 21). The platform integrates data collection, analytics and databases upfront, allowing for application development without dependencies that also would normally include application servers and cloud storage.

The open-source distribution of the platform includes a runtime for building stateful applications along with a framework, software development kits and a client for building streaming data visualizations and embedding those components into existing applications.

Also included are streaming APIs based on the WARP protocol, billed as the foundation of the only “streaming-first API.” That capability is intended to combine data streams and analytics with existing interfaces or external applications.

“Stateless, REST-based architectures are quickly overwhelmed by high data volumes and storage costs,” said Chris Sachs, founder and chief architect of The WARP protocol provides a “highly efficient, stateful way to manage streaming data and to build applications that are continuously in-sync with the real world,” Sachs added.

The company challenges the traditional model of uploading data to the cloud for storage and machine learning as the best way to crunch huge data volumes generated by edge devices.

“There is this kind of fiction out there advanced by MicrosoftGoogle, and Amazon that everybody is going to get data scientists and build some sort of machine learning model and push it to the edge,” Simon Crosby, CTO of, told Datanamilast year. “It’s just nonsense. It’s just not going to happen.”’s enterprise version is used for IoT as well as manufacturing and energy applications that increasingly integrate edge devices. The startup works with several systems integrators and cloud partners, including Microsoft Azure (NASDAQ: MSFT).

The community version of the platform is available here.

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