May
2

Why Scale-Out NAS is the Answer to Your Big Data Problem

Why Scale-Out NAS is the Answer to Your Big Data Problem

May 2
By

Big data is a double-edged sword. One edge of that sword shines bright with razor sharp insights and limitless reach. The other edge is rife with challenges and uncertainties. Ever-increasing volumes have many firms struggling to get a handle on their data. And with the influx of information flowing at such great velocity, controlling that data via traditional means is virtually impossible. Scalable storage is the answer, but the actual solution warrants some explanation.

The Scalability Debate Revisited

Our post on traditional NAS vs. scale out NAS explored how the two most common forms of scalability fit in the modern-day storage landscape. In a traditional (scale-up) storage architecture, the goal is to replace or upgrade existing components to build a bigger, faster, and more powerful storage system. By consolidating their storage resources, organizations can simplify storage management while conserving precious energy and data center space in the process.

While scale-up storage has its merits, this approach is not without its flaws. No matter how many modifications you make, there will eventually come a point when your existing system is maxed out and adding new capacity is no longer an option. Even a state of the art server only has so much room for expansion, so the need to purchase additional hardware quickly becomes a reality as the data continues to pile up. Moreover, a scale-up storage architecture can become cost-prohibitive and complex when factoring in the need to manage a host of individual systems.

Efficiency Beyond Scalability

Scale out storage in cloud connected to other device concept image

Nestled in the cloud, scale-out NAS (Network Attached Storage) can provide both the room necessary for growth as well the performance needed to meet individual business demands — all without placing additional strain on existing IT resources. But scalability is just one potential benefit. Scale-out NAS also makes it possible for organizations to focus more on managing their data and less on managing their storage. The more efficient the system, the less capacity required for data storage. As a result, capital expenditure and ongoing costs are reduced while storage remains simple to maintain.

From Potential to Reality

Scale-out NAS can provide the stable foundation needed to put your data assets to good use. However, that’s in a perfect world. Whether this potential is realized in a real-world scenario will depend on the overall efficiency of your storage infrastructure. With that in mind, here are some qualities to look for in a scale-out NAS solution:

  • Simplicity: The beauty of a scale-out architecture is the ability to incorporate technology that enables multiple components to function as a single storage system. With the right software, 1 petabyte of data should be as easy to manage as 1 terabyte.
  • Flexibility: A big data environment demands both scalability and flexibility from a storage system. The solution should scale based on your needs, preferably on a pay-as-you-grow basis.
  • Efficiency: The cold hard truth is that most storage devices advertise more space than they allocate to actual data storage. To deliver optimal efficiency, a scale-out NAS solution must go beyond the limitations of traditional hard drives and allow you to the get most from your storage capacity.
  • Availability: In theory, a storage architecture built on commodity hardware is more susceptible to failure. A scale-out NAS vendor should keep this in mind with built-in resilience and data protection features that maximize availability.

In today’s digital world, inefficient data management can have a direct impact on the efficiency of IT operations. Offering the promise of seamlessly scalability and simplified management, scale-out NAS is steadily changing the way the enterprise approaches data storage. Find a vendor that can tailor a solution to your needs and it could be the answer to your big data woes.