FEATURE DATA MANAGEMENT
was provisioned, wants to access the data, this kickstarts a considerable process which brings with it additional storage management overhead and the possibility of the dreaded data migration.
In summary, the customary attachment of storage arrays to particular application stacks results in captive silos of data on one hand, and the uncontrolled and ungovernable proliferation of data on the other.
The impacts are felt in increased costs in terms of management overhead and storage capacity, increased vulnerability to cyberthreats due to a greater surface of attack, and a corporate body of data that is more difficult to access with analytics and AI to gain insights.
Have hyperscalers shown the way?
The obvious contrast to all this is how things work in the cloud, and especially among the hyperscalers. In those environments, storage is abstracted, with capacity pooled by performance tier, and managed by a unified control plane layer. It certainly makes this capacity easy to consume for the hyperscaler’ s customers, but it also results in very efficient resource utilisation for the hyperscaler itself.
Regardless, the challenge is to bring the abstracted and pooled model of operations to the enterprise and to transition from storage management to data management so that, instead of managing individual storage systems, storage is managed as a unified fleet, consistently and intelligently, across the whole environment. This enables the organisation to understand and pilot the data sets independent of the hardware that underpins it.
Abstracted, policy-based, API-driven
The next evolution in the enterprise data centre will be towards such a virtualised cloud of data. That can be enabled by the right storage technology and intelligent software that can harness all of the resources under its control and enable the so-called‘ cloud operating model’.
A virtualised data cloud needs a unified control plane, with intelligent, autonomous data management and governance across the entire information system, whether onpremises, in the public cloud, or across both.
API driven and policy-based, it enables self-service, consistency of operations, integration with the entire application landscape and easy governance, regardless of the scale of the information system, while eliminating silos. That stands in stark contrast to the still very commonplace scenario of array-by-array management and manual operations.
The impacts are felt in increased costs in terms of management overhead and storage capacity, increased vulnerability to cyberthreats due to a greater surface of attack.
On the hardware side, capacity and performance must be able to scale dynamically and transparently, without downtime, performance degradation or unnecessary data migrations. It must also allow the organisation to offer various classes of services matching its policies in terms of performance, resilience, security and compliance, that can easily be consumed by applications, business units and internal customers, all orchestrated by the distributed control plane.
Data can then easily be shared, copied and distributed in a policy-driven, automated and efficient manner. to match the requirements of modern applications and AI use cases.
How to answer the challenge
So far there’ s only one architectural approach that has been able to tackle this
challenge; what we at Pure Storage define as the Enterprise Data Cloud( EDC).
EDC brings together a number of core pillars of functionality to create a unified data plane with an aim of providing a consistent experience across block, file and object in the customer storage fleet, alongside advanced data services such as snapshots, replication, multi-site highavailability and anti-ransomware features.
These capabilities are enabled by a common operating environment across array models. On top of this, intelligent insights and fleet-wide optimisation result from integrated AIops monitoring and analytics.
Next in the stack comes a unified control plane across pools of storage with different
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