Glassbeam SCALAR Set to Challenge Splunk

Another day, another Splunk competitor crops up with a weapon aimed to win the machine data hearts of the IT world. Can Glassbeam’s SCALAR knock Splunk off its perch?

For being an emerging field, machine data has been dominated by Splunk ever since it took its log file system and repurposed it to take advantage of the myriad of growing machine data available in the big data wild. The latest challenger is Glassbeam , which this week revealed SCALAR, a cloud-based platform for machine data analytics.

SCALAR, like Splunk, aspires to capitalize on the fast growing Internet of Things (IoT) phenomenon by offering an analytics platform that provides a central conduit for data collection and analysis. Built using open source tools, including the highly available Apache Cassandra database and search server Apache Solr, SCALAR aims to be a cloud based hyperscale platform that provides actionable insights from machine data.

The SCALAR system runs using Glassbeam’s own Semiotic Parsing Language (SPL), a definition language which the company says gives SCALAR the ability to “decipher hidden meaning inside machine data.” In a blog post last year, the company described its SPL as an intuitive language that describes the structure and semantics of a class of documents – thus giving it structure so that it can be analyzed.

Per Glassbeam:
“For semi-structured data, as is typical with our customers, it means we can utilize the structure inherent in the data and reflect it in a high-performance and highly normalized data warehouse. The SPL description of a class of documents and its semantics is passed through an interpreter to generate the database Data Definition Language (DDL) for staging, database DDL for the final warehouse, DDL and Data Markup Language(DML) for metadata to generate the UIs, and internal representations to perform a parse, a transactional or bulk load, and ETL transformations.”

While it’s a long-winded way of saying that its SPL technology does the heavy lifting in transforming the data into something its analytics engine can use, a tool that is able to automate all that actually would be quite powerful.

“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,” said Krishna Roy, research analyst at 451 Research in a statement. “With its core platform and applications, Glassbeam is well positioned to meet the growing demand for real time, multi-structured data analytics.”

Leveraging the SCALAR platform as a base, Glassbeam is layering on a few tools, including Glassbeam Explorer, which will serve as a discovery platform for the system giving analysts search access to visualize and explore the logs and other textual data in the system. Additionally, a visual development tool named Glassbeam Studio will provide users with a visual development tool that provides system managers with granular data mapping and metadata definitions access inside the core platform.

The company says that Glassbeam SCALAR and Explorer will be available on November 1, and that Glassbeam Studio is currently in beta. Details on pricing are not publically available.

Glassbeam last month announced that they had raised $3 million in additional equity funding, led by VKRM group, bringing their total seed funding to $6.1 million.

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