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Caringo CAS Selected by Johns Hopkins University

CAStor storage cluster supports active and archive data for genetic research.

Caringo Inc. announced that its CAStor software was selected by the Center of Inherited Disease Research (CIDR) at Johns Hopkins University to provide an affordable and scalable archive storage cluster for its growing volume of genotyping data.

CIDR is a centralized facility providing genotyping and statistical genetics services for investigators seeking to identify genes that contribute to human disease. Through a federal grant CIDR supports thirteen National Institutes of Health (NIH) and primarily concentrates on multi-factorial hereditary disease as well as analysis of single gene disorders. Changes in the practice of genotyping over the past several years have led to a substantial increase in the amount of data generated and the need to implement something more manageable than the JBOD storage servers it was using.

CIDR currently has a 31-node CAStor cluster implemented with 104TB of available capacity and 82TB in use. Having an affordable, high-performance and scalable CAStor cluster helps meet CIDR’s requirements in which it is producing 1TB of new data each week and sometimes daily. It can generate as much as 2.5TB of new data each day when operating at full capacity. “The benefits of CAStor – affordability, scalability, and ease of management – are an unbeatable combination for organizations with massive amounts of critical data,” said Mark Goros, Caringo CEO. “Johns Hopkins University’s CIDR is a data-intensive environment with a need for dependable, fast archiving of clinical information and CAStor’s acceptance is a testament to its unique capabilities.

CAStor is a scalable, high-performance and cost-effective software product that runs on standard, commodity server hardware for a fraction of the price of other CAS systems. CAStor is self-healing, managing and configuring, uses no proprietary APIs, and requires no provisioning or downtime to add nodes and storage capacity in real time.

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