Gartner 2017 Hype Cycle for Storage Technologies: Management SDS & Primary Data
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Gartner’s Hype Cycle for Storage Technologies 2017 report issued this week includes Primary Data as a vendor in the Management Software Defined Storage category. Gartner’s 2017 market assessment places Management SDS in the “innovation trigger” category which, as we have been seeing, is gaining momentum for significant growth ahead. For example, Gartner’s revenue forecast for Hyperconverged Integrated Systems grew from about a half billion dollars in 2015 at its Innovation Trigger phase, to about $2.5 billion dollars in 2017, with projections reaching $6 billion dollars in 2020.
In the 2017 Hype Cycle report, Gartner details the business value provided by Management SDS solutions, and DataSphere meets or exceeds the highest expectations for the value Gartner identifies for this category. Here, we examine highlights from Gartner’s report and how DataSphere can help enterprises better manage their data.
Superior Policy Management and Orchestration of Many Storage Resources
For context on the innovation category of Management SDS, it’s important to note that Gartner defines it as interacting with existing storage systems to deliver greater agility in storage services. This is distinct from Gartner’s definition of Infrastructure SDS, which creates and provides datacenter cervices to replace or augment traditional storage arrays.
To define the value of this emerging technology, Gartner notes: “Management SDS's ultimate value is to provide broad capability in the policy management and orchestration of many storage resources. While some management SDS products are focusing on enabling provisioning and automation of storage resources, more comprehensive solutions feature robust utilization and management of heterogeneous storage services, allowing mobility between different types of storage platforms on-premises and in the cloud.”
The storage and vendor-agnostic DataSphere architecture fully meets the criteria for the “more comprehensive solution,” as it connects different types of storage in a global namespace and automatically places data on the most appropriate storage resource regardless of vendor, including on-premises object or public cloud storage, according to the policies set by IT.
DataSphere exceeds the expectations for comprehensive capabilities in several ways. First, it enables enterprises to create policies that are focused on objectives expressed in ways that business and application owners understand. Performance can be defined in terms of application requirements for IOPS, bandwidth, and latency, protection defined in terms of “9’s” of availability, durability, and security.
DataSphere automatically places data on storage resources that can meet those needs. DataSphere admins can even create tiered service catalogs that can be associated with predefined chargeback or showback levels to streamline budgeting. Second, DataSphere moves live data automatically and transparently to applications. This means that DataSphere can move even active data dynamically, as business needs evolve, so unexpected workload spikes won’t endanger SLAs and IT no longer have to massively overprovision storage to protect performance.
I/O Optimization and Savings through Application Awareness
Gartner also emphasizes the importance of I/O optimization and how this can “reduce storage response times, improve storage resource utilization and control costs by deferring major infrastructure upgrades.” DataSphere delivers on both broad management capabilities and I/O optimization in one package.
Applications and storage have long been blind to each other’s capabilities and needs. The majority (if not nearly all) of today’s enterprise applications do not know the attributes of the storage where data resides. Applications cannot tell if the storage is fast or slow, premium or low cost. Conversely, storage does not know what data is the most important to an application. It only knows what was recently accessed, and uses that information to place data in caching tiers, which will increase performance if that same data happens to be accessed again. However, caching tiers do not have the intelligence needed to protect capacity for mission-critical applications, which can cause serious performance inconsistency or require more cache.
Metadata is the key to unlocking intelligence and awareness between applications and storage. DataSphere gathers metadata in real-time and applies machine learning to understand how applications experience storage (for example latency, IOPS and bandwidth). It also collects telemetry on the data that applications access, such as which files are open, closed, with modified dates and times, as well as any other metadata. With the ability to collect analytics from individual clients on single data objects, DataSphere can analyze application workloads, priority, historical trending, and available storage resources, comparing real-time activity against business objectives defined by IT and application administrators. DataSphere then automates data management for NFS, SMB, VMware ESXi clients across datacenter and cloud based resources, moving data to the most appropriate resource without application interruption.
In addition, DataSphere’s high-performance architecture eliminates common bottlenecks in modern storage systems to boost performance both at an aggregate and individual storage system level (for an example, see the recent blog post, Kick NAS Performance into High Gear). When all NAS systems are aggregated into a single global namespace, it becomes very easy to build a high-performance scale-out NAS architecture from a heterogeneous collection of storage arrays, allowing an application to read and write, in parallel, to the most appropriate storage systems at that moment.
Primary Data is proud to be included in Gartner’s Hype Cycle for Storage 2017, and we are even more pleased that we excel on delivering the value Gartner expects the Management SDS category to provide customers. Want to learn more? Contact email@example.com to schedule a meeting or demo.