Logentries Enhances Log Management And Analytics Platform With DevOps-Focused Collaboration Functionality

Log management and analytics vendor Logentries today announced an enhancement to its platform marked by the availability of a suite of collaboration features that improve the ability of teams to analyze and share insights regarding log data. Users of the Logentries platform can now annotate log data, share dashboards and send automated notifications to individuals and groups. The newly released collaboration functionality enhances the ability of the platform to serve the needs of DevOps teams that demand real-time agility with respect to log data analytics as well as the ability to communicate their observations regarding log data. The real-time collaboration functionality enabled by today’s release of the Logentries platform empowers DevOps professionals to more efficaciously identify root causes for issues such as system downtime, diminished application performance or networking-related bottlenecks as illustrated by the screenshot below.

The graphic above illustrates the annotation capability specific to today’s release. The annotation on the 404 Failure identifies an issue on a development server that may pertain to production servers as well. Logentries further instantiates the theme of accessibility and collaboration by enabling users to search log data using natural language and a click-through user interface that frees analysts from the need to write complex queries to understand the significance of log data. The platform also leverages a pre-processing engine that powers its analytics and data visualization capabilities in ways that deliver actionable business intelligence regarding real-time data. As told to Cloud Computing Today by Logentries CEO Andrew Burton, the Logentries platform can be used to understand data within on premise, public cloud, private cloud and hybrid cloud environments. The platform differentiates itself from the likes of Splunk, Loggly and Sumo Logic by means of enhanced data visualization and collaboration functionality that renders the platform amenable to business stakeholders that have little or no experience with scripting languages. Logentries plays in the hot machine analytics space with a platform whose rich analytics, collaboration and UI render it distinctive. Expect to hear more about the progress of Logentries as it builds on its 25,000 user base in subsequent months.

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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.