The data management company has created a way for companies to search petabytes of data across cloud and on-premises storage in minutes and take targeted actions
Komprise, the leader in analytics-driven data management as a service, introduced Komprise Deep Analytics Actions, a systematic way to find specific data across hybrid cloud storage silos and move just the right subset of data to rapidly feed data pipelines. For example, researchers at a pharmaceutical company can query and extract the files related to a specific experiment generated by a set of researchers, even if these files might be a small fraction of the petabytes scattered across datacenters and clouds, and then import this virtual data set into a data lake or data warehouse for further analysis.
Komprise Deep Analytics Actions is the next phase of evolution for Komprise Intelligent Data Management, tackling some of the most pressing problems with unstructured data today: it’s too large, encompassing billions of files and objects; too scattered across many storage silos with limited visibility across the silos; too hard to search for specific user needs such as analyzing car test data for a particular scenario, and too slow to move into environments where it can be used for analysis and manipulation or for cold data storage.
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Deep Analytics Actions: From Data Volumes to Data Value
Komprise Deep Analytics delivers granular, flexible search and indexes data in-place across file, object and cloud data storage to build a comprehensive Global File Index spanning petabytes of unstructured data. New Komprise Deep Analytics Actions leverages these virtual datasets for systematic, policy-driven data management actions, delivering the following benefits:
- Users only move the data they need, with the ability to create queries on countless file attributes and tags such as: data related to a specific tag or project name, projects that are no longer active, file age, user/group ID’s, path, file type (aka JPEG) and specific extensions, data with unknown owners.
- Eliminates the manual effort of finding custom data sets and moving them separately from different storage silos since Komprise can create a virtual data set based on the query and systematically and continuously move data from multiple file and object silos to the target location.
- Improves IT and business collaboration around data, as data owners/users can participate in data tiering decision-making by tagging files and creating their own queries from any combination of tags and metadata.
“We see a lot of use cases for deep analytics actions at the University,” says Matt Madill, senior storage administrator at Duquesne University. “For instance, different research groups have unique requirements which users can support with tagging so that those data sets can not only be discovered easily but they can apply the appropriate data management policies to them for long-term storage. We’ll be able to give users the power to have better control of their data and let us know what to archive and when. Komprise is helping us make smarter decisions on our data and that is a competitive advantage.”
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“Unstructured data has been and continues to grow exponentially. Time to actionable insights is a major factor in realizing actual financial value, a.k.a. time-to-value,” says Marc Staimer, president of Dragon Slayer Consulting. “Komprise solves the very real, concrete time problem that comes from moving petabytes of file data into a cloud data warehouse. With their “Deep Analytics Actions,” you can simply find and move a small fraction of what’s required to get the real-time analytics needed, which speeds up that time-to-value.”
“With Komprise Deep Analytics Actions, departmental users can maximize the business value of their unstructured data by leveraging their domain knowledge to cull and find the right data sets to operate on across all their silos,” said Kumar Goswami, co-founder and CEO of Komprise.
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