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Katana Graph raises $28.5 million to handle unstructured data at scale

Katana Graph, a startup that helps businesses analyze and manage unstructured data at scale, today announced a $28.5 million series A round led by Intel Capital.

Katana Graph was founded by University of Texas at Austin computer science professor Keshav Pingali and assistant professor Chris Rossbach. The company helps businesses ingest large amounts of data into memory, CEO Pingali told VentureBeat in a phone interview. The UT-Austin research group started working with graph processing and unstructured data two years ago and began by advising DARPA on projects that deal with data at scale. Katana Graph works in Python and compiles data using C++.

Like companies that deal with algorithm auditing, AIOps, and model monitoring and management services, a range of startups have emerged to help businesses handle analyze and label data, which may be why Labelbox raised $40 million and Databricks raised $1 billion.

Katana Graph is currently working with customers in health, pharmaceuticals, and security.

“One of the customers we’re engaged with has a graph with 4.3 trillion pages, and that is an enormous amount of data. So ingesting that kind of data into the memory of a cluster is a big problem, and what we were able to with the ingest time is reduce the ingest time from a couple of days to about 20 minutes,” Pingali said.

Today’s round included participation from WRVI Capital, Nepenthe Capital, Dell Technologies Capital, and Redline Capital.

Katana Graph was founded in March 2020 and is based in Austin, Texas. The company has 25 employees and is using the funding to expand its marketing, sales, and engineering teams.

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