Automate the deployment of IT services, optimize IT operations, and reduce TCO with Software-Defined Storage
With organisations both public as well as private becoming digitized in India and the Government of India’s increased focus on digitization, there has been an unprecedented surge in the amount of data generated. To effectively leverage the different kinds of data generated for the benefit of the business, Software-Defined Storage (SDS) offers enterprises a path to effectively address this explosive growth.
SDS is the intelligent application of distributed computing techniques to the design of storage systems. Combining the power of distributed computing with the use of commodity hardware plus innovative software that optimises storage space efficiency, performance efficiency, manageability, and scale, it provides the essential ingredients necessary to cost-effectively manage explosive data growth. SDS solutions are elastic, scale-out, software-based storage systems designed to run on commodity hardware, with in-place, multi-protocol data access and built-in geo replication mechanisms all wrapped in a simplified and far more scalable management experience.
Understanding the importance of a portfolio approach, Dell EMC offers SDS solutions that are suitable for a variety of use cases, application workloads and customer requirements. Dell EMC develops new SDS products such as ScaleIO, ViPR and Elastic Cloud Storage (ECS) and also preserves the legacy of its tried and trusted storage products such as Isilon and VNX by offering them in the form of software-defined products that are free to try. To assist enterprises, Dell EMC offers an industry-leading portfolio
of SDS solutions for –
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Orchestration and management of datacenter resources
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Storage, management and in-place analytics of unstructured data
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Scalable storage of structured data and virtual infrastructure
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Infrastructure for cloud-scale, geo dispersed workloads
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Repository for analytics-centric workloads
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Storage for hyper-converged infrastructure
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Infrastructure for workloads that require data adjacency
