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Komprise today introduced its solution to organizations trying to make sense of large-scale distributed datasets, including files and objects distributed across cloud and local storage, as well as across multiple public and private clouds or multiple storage layers.
Komprise Deep Analytics Actions, a new component of the Komprise Intelligent Data Management platform, is designed to find and index relevant data, no matter where it resides, and consolidate it for further analysis. For example, researchers at a pharmaceutical company can query and extract files related to a specific experiment generated by a set of researchers. Researchers can then import this virtual dataset into a data lake or data warehouse for further analysis, according to Komprise.
Compressor, President and COO Krishna Subramanian said in an interview that Pfizer is one of several pharmaceutical companies (including “80% of the people who build COVID vaccines”) that use the Komprise platform, which helped define the requirements for Deep Analytics Actions. The technology can be used in advance to determine where data should be stored or on-demand for a particular analytical application.
“For example, an autonomous car manufacturer may have different data centers where they collect all their data, petabytes and petabytes thereof, and will just collect data on how the vehicle behaves at red lights, which can be 3% of that,” said Subramanian. Komprise can now create a centralized index based on data, no matter where it is stored, classified by both metadata (an explicit tag or the file’s creation data, type or owner) and extended metadata (such as a project name encoded in the file name or directory where a file is saved), she said.
The cost of data consolidation and analysis
In the absence of automation and precise targeting, the data consolidation step can be time consuming and expensive due to data output fees that cloud providers charge when exporting data from a cloud provider. This problem is so common that it sometimes prevents companies from pursuing potentially fruitful data analysis, according to Komprise. With Deep Analytics actions, only the data required for a particular analytical task is moved.
“We see many cases of use for deep analysis at the university,” said Matt Madill, senior inventory administrator at Duquesne University, in a quote to the press release. “For example, different research groups have unique requirements that users can support with tagging so that these datasets can not only be easily detected, but they can apply the relevant data management policies to them for long-term storage. We will be able to allow users to have better control over their data and tell us what to archive and when. ”
The privately owned Komprise was founded in 2014 and has raised $ 50.7 million so far. Other components on the platform help with tasks like data migration to the cloud. Subramanian discussed the broader issues of unstructured data analytics in a July interview with VentureBeat.
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