KNIME launches pay-as-you-go offering on KNIME Community Hub
KNIME, the software company focused on making working with data intuitive, announced a new software as a service (SaaS) offering that lets users run and automate workflows on KNIME Community Hub.
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“So far, KNIME Community Hub has been an important part of our open ecosystem, as an easily accessible repository to find and share solutions and collaborate on data science workflows”
The open source KNIME Analytics Platform is available for free with full functionality, allowing users from all backgrounds and experience levels access to analytics with its intuitive interface. Over the years, KNIME Community Hub has brought together a thriving global community around the open source platform with users upskilling, sharing and collaborating on thousands of data science solutions.
Today, KNIME announced the addition of new features to KNIME Community Hub. The latest release allows teams to not only share and collaborate privately on visual workflows, built using KNIME Analytics Platform, but also lets them automate those workflows, run them ad-hoc, or run them as data apps. Analyses of any level of complexity can be built once and scheduled to re-run every week, hour, or minute. Available as a SaaS offering, teams pay only when they run workflows, and no IT setup is required. With features like versioning, teams can easily track who changed what and revert back to a previous version if they need to.
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“So far, KNIME Community Hub has been an important part of our open ecosystem, as an easily accessible repository to find and share solutions and collaborate on data science workflows,” said Michael Berthold, founder and CEO of KNIME. “With the new SaaS features, we now allow the community to collaborate in small teams and easily execute their workflows in the cloud.”
Teams that want a dedicated KNIME Hub set up on their own infrastructure can opt for KNIME Business Hub, the dedicated software for enterprises. Teams of all sizes can securely collaborate and share best practices as well as deploy and monitor their analytical workflows.
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