The acquisition of Cloudmeter rounds out Splunk’s portfolio with a capability to analyze machine data from a wider range of sources. Financial terms of the deal were not disclosed. The transaction was funded with cash from Splunk’s balance sheet, the company said.
Indeed, the addition of Cloudmeter will enhance the ability of Splunk customers to analyze machine data directly from their networks and correlate it with other machine-generated data to gain insights across Splunk’s core use cases in application and infrastructure management, IT operations, security and business analytics.
“The increasing complexity of enterprise applications and infrastructure makes network data a valuable source of data for operational intelligence,” Godfrey Sullivan, chairman and CEO of Splunk, said in a statement. “Cloudmeter has a strong technical team with deep networking expertise. They have built a robust, highly scalable solution that will allow our customers to easily capture network data, either on-premises or in the cloud. We look forward to integrating Cloudmeter technology into Splunk’s platform for machine data.”
Founded in 2007, Cloudmeter was one of the early companies to identify the value in the massive amounts of data that flow through organizations’ networks. Cloudmeter helps customers harvest network traffic to create actionable insights across a broad range of use cases.
“Our focus has been to develop innovative solutions that enable easier access to and insight from network data,” Michael Dickey, founder, CEO and CTO of Cloudmeter, said in a statement. “Network data is a major contributor to the growth of big data, and we are excited to join Splunk. We look forward to developing new capabilities that will help customers realize the full potential of their network and other machine-generated big data.”
The addition of Cloudmeter gives Splunk a leg up in the machine data analytics arena. However, Splunk’s strength remains on the IT side of this market, whereas some machine data analytics companies, such as Glassbeam, are focusing less on IT and more on the Internet of things (IoT) side of the machine data analytics space. Right now, the market for machine data analytics of IT information is far larger than that for IoT data, analysts say.
Last month, Glassbeam announced Glassbeam SCALAR, a cloud-based platform for organizing and analyzing complex machine data. The company also introduced Glassbeam Explorer, a cloud application that provides search and explore capabilities to the core platform, and Glassbeam Studio, a visual development tool to map unstructured logs for analytics and prediction.
The IoT is made up of an ever-growing number of connected devices, which generate vast amounts of data that are complex in variety, volume and velocity. Product manufacturers across data-intensive industries—such as storage, wireless, networking and medical devices—are struggling to make sense of all this data and turn it into proactive business intelligence.
In an interview with eWEEK, Puneet Pandit, CEO of Glassbeam, said the company’s solutions are designed to bring structure and meaning to data from any connected device. With its core analytics platform, Glassbeam goes beyond traditional log management and data mining tools, and beyond operational intelligence to unlock the value of “true device” data for actionable product, customer and business intelligence.
Splunk Buys Cloudmeter to Boost Operational Intelligence Portfolio
“Our solutions are already deployed and trusted by some of the world’s largest Fortune 500 companies,” Pandit said. “There is more to log analysis than IT logs and operational intelligence, and our ultra scalable platform takes the next step to unlocking the value of machine data – product and customer intelligence for the entire enterprise. We are proudly at the forefront of machine data analytics for the next era and the Internet of Things.”
Glassbeam SCALAR is a scalable platform for machine data analytics – a hyper scale cloud-based platform designed to organize and analyze unstructured and multi-structured machine data.
The SCALAR platform is powered by a parallel asynchronous engine, which uses the company’s domain-specific language (DSL) to describe the structure, meaning and relationships of unstructured data. Leveraging a stack of open source big data components including Cassandra and Solr, Glassbeam SCALAR is built for scale and speed to handle complex log bundles and analyze terabytes of data.
“Glassbeam continues to innovate in machine data analytics,” said Krishna Roy, research analyst at 451 Research, in a statement. “In today’s big data reality, Glassbeam addresses one of the key issues around unstructured data, which is how to translate complex machine data into something structured and meaningful. With its core platform and applications, Glassbeam is well positioned to meet the growing demand for real time, multi-structured data analytics.”
The company provides a set of standard applications, dashboards and tools to extend the power of the Glassbeam SCALAR platform. These include Glassbeam Analytics, Glassbeam Health Check, the new Glassbeam Explorer application and the new Glassbeam Studio visual development tool.
Glassbeam Explorer is a cloud-based search and log management application that enables users to find, explore, analyze and visualize logs, and other textual data. Users can manage and integrate complex, multi-format data from various sources to isolate and troubleshoot issues, compare and correlate data to tie events to changes, and create a central repository of device and customer logs for compliance and audit purposes.
Meanwhile, Glassbeam Studio is a visual development tool that uses advanced extract, transform and load (ETL) and semantics to map unstructured logs to structured data for deeper analytics. Glassbeam Studio will enable developers to generate data mapping and metadata definitions inside the core platform, leveraging Glassbeam’s DSL for increased productivity and customized application development.
“Aruba Networks leverages machine data from thousands of systems in the field, and the Glassbeam platform has enabled us to quickly extract valuable business insights from these logs,” said Ash Chowdappa, vice president of product management at Aruba Networks, in a statement. “We use the Glassbeam apps to obtain product and customer intelligence that allows us to proactively understand, support and manage our installed base and adopt a data-driven approach to business decision making.”
Glassbeam’s Pandit explained that in his view Splunk is great for analyzing Syslog data in the IT space. Syslog is a standard for computer message logging. However, “Our data is a lot more complex and multi-structured,” he said. “We take different varieties of data and put it in a relational model. Our competition is largely a ‘build versus buy’ scenario. Some of our prospects say they tried to use Splunk, but it doesn’t scale to handle multi-structured data.”
Glassbeam takes its solution to market as a Software as a Services (SaaS) solution using the Amazon Web Services (AWS) cloud, Pandit said. “But we have some people who want to OEM the solution for their private cloud, we can do that,” he said. “We offer some flexibility. For instance, we can take out Cassandra and add in Hadoop if you want.”