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Kyvos Insights Allows Businesses to Seamlessly Scale Business Intelligence in the Cloud

By   /  November 19, 2018  /  No Comments

A recent press release reports, “Kyvos Insights, a big data analytics company, today announced the availability of Kyvos Version 5, providing businesses with a cloud native way to elastically scale and draw intelligence from exponentially growing data workloads. The platform update delivers enterprise-class OLAP with instant response times and unlimited scalability, allowing enterprises to glean critical insights from today’s largest data sets while also enabling real-time queries to be run on data as soon as it arrives. The new platform updates have been designed to better allow business users to gain real-time intelligence from ever increasing data sets. Developed specifically for the cloud, the updates expand upon the commitment to scalability inherent in the Kyvos platform by introducing elastic scalability for querying, allowing businesses to scale up and down more seamlessly as data loads change. This elasticity also allows for improved segmentation of data workloads within organizations, meaning different business units can more readily run queries without slowing down other teams.”

The release continues, “Kyvos Version 5 updates also allow businesses to run more real-time queries on data before it enters the cube, as well as gain deeper insights on query patterns and data profiles, allowing for the development of smarter data cubes. Key highlights of the Kyvos Version 5 update include: (1) Elastic OLAP designed natively for the cloud: Companies can now more easily and affordably scale up and down elastically with no disruption to deal with peak workloads and usage patterns. (2) Smarter analytics: Analysis of query patterns provides insights into how data is being used by business analysts, and this is used for auto tuning and guidance for optimized cube design. (3) Data profiling: Kyvos provides the ability to profile data that is stored in data lakes and use machine learning to optimize how the data can be transformed or added to the cube.”

Read more at PR Newswire.

Image used under license from Shutterstock.com

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