Glassbeam Introduces Data Transformation and Edge Computing Capabilities for IoT

Glassbeam, a machine data analytics company, has introduced two product enhancements for the IoT analytics market.  The new capabilities are aimed at automating the transformation of unstructured machine data into business insights and also providing a lighter footprint for Glassbeam at the edge.

According to the vendor, the opportunity presented by IoT analytics is the ability to transform and act on new machine information in real time. However, what is needed to make that opportunity a reality better data transformation and analytics tools. 

Glassbeam Studio is a data transformation and preparation tool focused on automating the manual work required to convert unstructured, raw machine log data into actionable information.

It provides a drag-and-drop interface that allows users to model and transform any kind of log format complexity. The solution can help improve IoT analytics by reducing the time it takes to design, implement, and maintain an end-to-end IoT solution by a factor of 100x, according to the vendor.

A second solution, Glassbeam Edge offersIoT analytics at the edge through a platform that enables device manufacturers to perform mission-critical activity without dealing with the costs or latency involved in sending data back to a central cloud.

The availability of edge computing capabilities through a lightweight version of its platform that ingests, parses, and analyzes unstructured data in close proximity to the actual device is intended to decrease the costs and delays involved in sending large quantities of data back to centralized data centers. According to Glassbeam, these capabilities are particularly useful for high growth IoT areas such as predictive maintenance in smart grids, the oil and gas sector, and power generation.

The two products, Glassbeam Studio and Edge, alleviate two of the biggest pain points in the IoT industry, according to Puneet Pandit, co-founder and CEO of Glassbeam. With the additional capabilities, he noted, the platform now has “the most complete set of functionality for tackling the most daunting analytics challenges for machine data.”

Glassbeam, a partner of ThingWorx Ready program, will also be working closely with ThingWorx Machine Learning platform to take advantage of the functionality of Glassbeam Studio and reduce time to draw insights in machine learning projects.  According to Glassbeam, by some industry estimates, more than 60% of time in a typical machine learning project is spent in data preparation and transformation before any meaningful analysis can be performed, and particularly an issue for machine learning projects with unstructured log data where data scientists’ valuable time is spent on mundane repetitive tasks as opposed to building valuable models and predictions. 

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