Redis Labs Named a Challenger in Gartner 2020 Magic Quadrant

Redis Labs Named a Challenger in Gartner 2020 Magic Quadrant

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Redis Labs, the home of Redis, today announced the company has been named a Challenger in the 2020 Magic Quadrant for Cloud Database Management Systems1. Gartner evaluated Redis Labs for its product Redis Enterprise Cloud among managed cloud database services from 15 additional vendors, with Redis Labs being positioned furthest to the right on the Completeness of Vision in the Challenger quadrant axis.

“We are proud to be positioned as a Challenger in this new Magic Quadrant which analyzes the biggest and most critical market shift happening today––the rapid growth of fully managed cloud database services,” said Ofer Bengal, Co-Founder and CEO at Redis Labs. “We believe Redis Labs is well positioned to shape this market with our focus on delivering a truly unified real-time data platform. By supporting modern data models, across any cloud or hybrid deployment, and underpinning of high-throughput low-latency transactions, Redis Enterprise Cloud is designed for not only today’s traditional database use cases, but also emerging AI/ML processes which require the real-time performance.”

Redis Enterprise Cloud is a cost-effective, fully managed Database-as-a-Service (DBaaS) available on AWS, Microsoft Azure, and Google Cloud. Built on a serverless concept, Redis Enterprise Cloud simplifies and automates database provisioning so software teams can focus on building new innovations, rather than the operational complexity or service availability of their infrastructure. Designed for modern distributed applications, Redis Enterprise Cloud unifies data across any deployment model and the globe, enables seamless migration of datasets, and offers elastically with five-nines availability. Redis Enterprise Cloud delivers these capabilities while maintaining the sub-millisecond performance expected from Redis, at a virtually infinite scale, and offering endless possibilities through native data structures and modern data models.

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