storage ICT
To address these and future requirements a new market category has emerged; Data Defined Storage. Data Defined Storage transforms the way organizations manage, scale, protect, search and gain value from unstructured data by uniting application, information and storage tiers into a single, integrated data centric management architecture
requirements a new market category has emerged; Data Defined Storage. Data Defined Storage transforms the way organizations manage, scale, protect, search and gain value from unstructured data by uniting application, information and storage tiers into a single, integrated data centric management architecture. This data centric approach is a complete paradigm shift away from legacy media centric storage architectures.
Unlike Object Storage, Data Defined Storage represents a single, holistic, and comprehensive approach to ALL unstructured data and information in the global enterprise. It includes the ability to manage disparate data types across large, highly distributed locations, end- user data management and capabilities for Big Data analytics built into the solution. A Data Defined Storage methodology results in a comprehensive, all-encompassing solution for leveraging the value of data as a strategic business asset. It automatically archives and migrates data, providing a distributed repository of all unstructured data consolidating storage pools, file systems, emails, PST files, SharePoint and other content into a single view through file system virtualization for accelerating e-Discovery.
By eliminating the performance bottlenecks and resilience issues typically found in the object storage space, where 3rd party file gateways, access control schema and proprietary APIs for integration are all too common.
Data Defined Storage builds a protocol agnostic grid-level POSIX- compliant global namespace and object virtualization layer. In doing this, all the common file transport protocols, including CIFS, NFS, as well as the object storage APIs, are exposed. To improve performance over standard NAS and file servers, a grid system of nodes is used. Every node in the grid can directly service standard file protocol access requests, eliminating the need for failure-prone and performance-reducing gateway servers. Not only that, but due to the linearly scalable Data Defined Storage architecture, every node delivers parallel I/O, improving the performance across the grid.
A Data Defined Storage approach provides an abstraction layer whereby data is no longer accessed or identified by the storage device upon which it currently resides. This allows use of the fastest enterprise disk arrays, cloud storage targets, or tape targets. To accomplish this, a methodology for identifying data based on the nature of the content and the value of
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that content, rather than its physical location is deployed. Policy management on the metadata level enables the placing and re- placing of data upon the most appropriate storage device pool or tier over time, without causing a loss of accessibility, security, or referential integrity. A Data Defined Storage solution is designed with distributed access in mind. Typically, object storage systems have a single access point. Although data itself may be distributed for disaster prevention purposes, the access to that data is often provided by a single centralized metadata server. Whereas Data Defined Storage delivers distributed repositories for data and metadata, exposing both to remote sites, and allowing access from anywhere.
As with object storage, the basic file metadata is extracted and stored separately to the data, but unlike object storage, the deep content metadata is indexed, as are arbitrary custom metadata tags, enabling distributed enterprise content search (e-Discovery) and analytics. Data Defined Storage embeds this intelligence inside the storage infrastructure, and through metadata and metadata tags, it maximizes value for the organization through enhanced search and data analytics.
Summary
Object storage technology is a good foundation to address massive data growth challenges, but it is only a component technology that contributes towards a total solution. A file gateway is often needed to allow existing file based workflows to continue to operate. Data capture software may be needed to find suitable files and move them to the object store. Also, to find relevant files later an e-Discovery solution may also need to be added to the stack.
Data Defined Storage, as embodied by Tarmin GridBank, goes beyond the basic technology components found in many common object storage products. Its fully integrated solution stack delivers scalability, parallel object access, architectural flexibility, and improved data availability, without the need for 3rd party products.
It provides a data centric storage solution that breaks down the barriers that previously encumbered unstructured data storage and allows organizations to effortlessly meet performance and scalability needs, today and in the future, while significantly reducing corporate risk and monetizing data assets.
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