Glassbeam SCALAR Delivers Machine Data Analytics And BI For Internet Of Things

Today, machine data analytics vendor Glassbeam announces the release of Glassbeam SCALAR, a cloud-based platform that specializes in machine data analytics for the internet of things. In contrast to machine data analytics vendors such as Splunk and Loggly that focus on analytics related to machine data specific to data center and cloud-based IT environments, Glassbeam concentrates on complex, multi-structured log data from medical devices, sensors and automobiles in addition to data center devices.

The SCALAR platform leverages open source Big Data technologies such as Cassandra and Solr to deliver actionable business intelligence derived from structured and unstructured data. Meanwhile, SCALAR’s parallel asynchronous engine allows the platform to scale horizontally to process massive amounts of machine data that are interpreted by way of its dynamic schema functionality and semantic technologies that enrich the platform’s ability to make connections between source data elements.

A key component of the Glassbeam SCALAR platform is Glassbeam Studio, a tool that allows developers to transform unstructured machine data into structured formats for the purpose of performing a deeper dive into incoming data. Glassbeam Studio positions developers to transform data into Glassbeam’s proprietary semiotic parsing language (SPL) for understanding machine data before it is digested by the Glassbeam SCALAR platform and transformed into visual analytics as illustrated below:

Glassbeam’s output consists of visually rich, business intelligence dashboards that enable customers to make proactive, strategic decisions by way of a dynamic, real-time dashboard that illustrates trends and key attributes of source data. Key features of the Glassbeam platform in relation to the larger landscape of machine data sources and modalities of deployment are as follows:

Given that the internet of things remains to be fully realized, the release of Glassbeam SCALAR represents a bold attempt to carve out an early niche in the space by focusing on business intelligence in relation to machine data. Nevertheless, the underlying technology platform impresses by way of the breadth of its functionality as exemplified by Glassbeam Explorer, the platform’s new search and discovery tool for understanding, analyzing and visualizing machine data. Expect Glassbeam to continue to innovate and differentiate itself from the rest of the machine data analytics space, even as it captures market share related to data center devices such as servers, routers and firewall technologies. In the meantime, the race is on with respect to who will capture the early foothold in the internet of things Big Data analytics space as the industrial internet matures and gradually renders itself more and more ubiquitous in the lives of enterprises and consumers alike. Pivotal One has already placed its bets in the Internet of Things analytics space thanks to a $105 million investment from GE, but the landscape still remains wide open.


Glassbeam Raises $3M To Support Its SaaS Machine Data Analytics Solution

Machine data analytics company Glassbeam recently announced the finalization of $3M in equity funding in yet another affirmation of the vitality of the machine data space. The funding raise was led by the VKRM group. Glassbeam specializes in transforming analytics on machine data into actionable business intelligence that customers can use for a variety of use cases including optimizing application performance, more effectively understanding their customer base or making broader strategic decisions. The funding will be used for product development, the expansion of Glassbeam’s sales operations team and creation of a data science team dedicated to developing algorithmic insights from Big machine data. Glassbeam delivers its machine data analytics solution by way of a SaaS platform that provides a suite of customizable dashboards on topics such as performance or capacity management with respect to a specific IT infrastructure. Insights are derived through its semiotic parsing language (SPL), a domain specific language used for understanding relationships between elements of machine data. Glassbeam competes with the likes of Sumo Logic, Splunk and SaaS machine data analytics vendor Loggly. Current customers include IBM, Hitachi and EMC.

Loggly Raises $10.5M And Releases Second Generation Log Management Platform

Loggly, a cloud based, log data analytics platform, today announced the finalization of $10.5M in funding from Cisco and Data Collective with additional participation from existing investors Trinity Ventures, True Ventures and Matrix Partners. Loggly performs analytics on machine data from applications and platforms and transforms that data into ways that IT administrators and business stakeholders can consume. The principal use case for the company’s log management platform concerns the analysis of log data generated by cloud-based applications that generate massive amounts of log data on a daily basis in verticals such as ecommerce, gaming and social media. Today, Loggly also announced the release of its second generation log management platform which it expects will drive growth at multiples that exceed the five fold increase in growth that it experienced in comparison to last year. Loggly differentiates itself from competitors such as Splunk by way of its SaaS infrastructure, and focus on solutions targeted towards web-based traffic in addition to its enhanced data visualization functionality. According to VentureBeat, Loggly was instrumental in helping President Obama earn re-election by providing his campaign staff with insight into web traffic specific to its cloud based election software applications. Current customers of San Francisco-based Loggly include Intuit, PGI, Salesforce, Samsung and Uber.