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.