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How to address challenges associated with persistent data growth, limited budgets

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Faced with the ongoing challenge of relentless data growth coupled with only small (if any) storage budget increases in the coming 12 months, data professionals have a dilemma: how to intelligently rethink the expansion of storage infrastructures in this age of unprecedented budget scrutiny. The key, according to experts at FalconStor Software, Inc. is using agnostic approaches with predictive intelligence to evolve data centers without lock-in.

According to a recent analyst report, approximately half of the firms surveyed expect to see a data growth rate of 25-percent or more over the next twelve months. As data center professionals examine options to address growth, they must be prepared to move beyond traditional NAS and SAN appliances and leverage newer technologies including software defined storage approaches, which can provide a solution. But there are challenges.

Business happens. Infrastructures that start out as homogeneous quickly become heterogeneous, and management and orchestration across multiple storage platforms and differing hardware can create major headaches. Siloed data exists, lacking heterogeneous, horizontal integration.

The answer to addressing these challenges is to decouple from the existing hardware; intelligent management and predictive analysis can then be added, bringing the real-time understanding that allows for cost savings and the ability to do more with less. Infrastructural freedom provides flexibility; the ability to leverage a heterogeneous environment without lock-in is the key to being able to evolve infrastructures while addressing budget constraints.

Managing heterogeneous storage is not new, but the ability to manage heterogeneous storage across a heterogeneous network infrastructure (regardless of underlying hardware) is revolutionary.

Virtualization is the foundation, but it is only a start which serves as a jumping off point to enable layering of data services, intelligence and predictive analytics that allows business to make their data actionable across the varied infrastructure.

“Data professionals today are tasked with managing a very mixed environment that includes spinning disks, commodity hardware, SSD and flash that are siloed as a result of company growth, business acquisitions, technology advancement and piecemeal acquisitions,” said FalconStor’s VP of Technology, Farid Yavari. “Our approach, which is truly agnostic and unique in the industry, offers analytics and insight across heterogeneous storage environments – allowing users to take both proactive and reactive action to better manage capacity, performance, availability, and budgets.”

Intelligent abstraction and predictive analysis are critical for smart infrastructure growth, and the software platform must be truly hardware-agnostic. The ability to understand network issues – current and future – allows data center professionals to move specifically performing resources to appropriate tiering, correct network latency issues and determine specifically when expansion needs to occur - all vital issues related to infrastructure planning and efficient budget use.

Being able to have a current and predictive view of storage resources across a heterogeneous environment is a critical requirement to successfully manage that environment. Managing and monitoring in a deliberate manner with historical, real-time and predictive views takes the guesswork out of new storage purchases, enabling the ability to take intelligent action and eliminate surprises.

We live in a heterogeneous world, and the ability to manage mixed environments that can span from hybrid to virtual, cloud to VMware as well as future technologies is the only approach that allows for flexibility and agility without lock-in, according to FalconStor.

Real-time and predictive insight through heterogeneous analytics, up and down from client to physical storage regardless of vendor, is key to making smart decisions that can help storage professionals address the challenges that are associated with data growth and limited budgets.


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