The following is a guest post from Jonathan Ring, co-founder and CEO of Caringo.
Contrary to popular opinion, the world is not becoming more complex — the opposite is true. The fact that we create 2.5 quintillion bytes of new data per day simply means we are more capable than ever of identifying, analyzing and simplifying the complexities that come with immense amounts of information.
This capability is Big Data, and it is transforming industries worldwide. Ninety-three percent of companies worth more than $250 million rate it as either "important" or "extremely important" in their day-to-day operations. However, the obstacles are as significant as the opportunities.
Capitalizing on Big Data is a struggle. And the first stumbling block for most enterprises is trying to scale storage capacities to match Big Data's wide footprint. After all, data cannot become "big" unless it can be effectively accumulated, and it cannot become useful unless it is properly managed and analyzed. Overcoming this hurdle requires a forward-thinking approach based on the emerging concept of "big storage."
Meeting the storage requirements of Big Data
Despite the widespread reliance on Big Data, many enterprises still use legacy storage solutions designed to meet 20th-century data requirements. Up to 53% users reported that the performance of their storage solutions is no longer adequate, according to Tintri's 2015 survey of 1,000 data center professionals.
These legacy systems — such as network attached storage systems — are not only cumbersome, but also difficult and expensive to upgrade. In addition, they generally lock companies into continuing to purchase expensive, proprietary hardware that cannot evolve with the needs of the business and are simply not suited to accommodating the depth and breadth of Big Data.
The data is too, well, big, and its variety of file types surpasses the capabilities of outdated technology. As a result, something as seemingly benign as data storage often proves to be a major inhibitor of growth.
This explains why so many forward-thinking leaders are moving to a new software-defined storage paradigm in search of a scale-out solution that will not bottleneck under Big Data's weight.
Big storage is an alternative uniquely capable of meeting the requirements of Big Data. It allows the establishment of a vast "data lake" that's free of file hierarchies and restrictive load limits and that's accessible by Hypertext Transfer Protocol.
Rather than straining to meet the needs of the present, big storage must be designed to exceed the needs of the future.
Delivering on Big Data's value
Microsoft CEO Satya Nadella estimated in 2014 that Big Data's value would reach $1.6 trillion as early as 2018. That figure is realistic — perhaps even somewhat conservative — considering Big Data's ability to improve customer relations, streamline operational efficiency, refine business intelligence and accelerate innovation.
However, none of these opportunities is possible if the IT architecture does not accommodate big storage for Big Data. Big Data requires a constellation of technologies and capabilities to live up to its promise. Right at the center of that constellation is the storage platform. What capabilities do you need in your big storage?
Here's a quick list of items to consider and why they are important:
- Speed: Much of Big Data's value is relative. To capitalize on it, relevant data must be instantly retrievable. Big storage provides the low latency necessary to act as quickly as possible.
- Flexibility: Big data must incorporate any and all relevant information to provide accurate insights. Big storage allows enterprises to capture data from multiple sources spread across distributed networks.
- Scalability: If history is any indicator, the scope of Big Data will continue to increase exponentially in short periods. Often, those increases will appear suddenly rather than incrementally. Big storage capacities can scale upward rapidly and without restrictive limits.
- Security: Big data's dark side is that vast repositories of sensitive data are both an appealing and vulnerable target for hackers. Big storage solutions allow enterprises to implement ironclad protections and disaster recovery capabilities.
- Searchability: It is not enough to just store data. Data storage also needs to use metadata and allow sorting and analysis in order to extract ongoing insights and monetary advantages. And search cannot be an afterthought; it needs to be native to the system's design.
If yours is among the 75% of companies currently investing in Big Data and you want to make certain you are doing everything possible to achieve your objectives, examine the foundation of your storage. While you are considering a switch, explore software-defined object storage that meets the requirements detailed above and that lets you seamlessly upgrade and evolve the underlying hardware infrastructure without proprietary vendor lock-in.
Deciding to switch to a new storage paradigm isn't easy, but Big Data's potential is too significant to ignore and, in today's competitive marketplace, imperative to harness.