The Big Data phenomenon has encouraged organizations to pursue all options when accumulating increasingly diverse information sets from highly disparate sources. The trend has essentially expanded the network and caused an influx of traffic. Unfortunately, conventional IT systems with minimal or limited bandwidth simply can’t live up to the constantly changing levels of data transit. This complication is causing some organizations to stop in their tracks, ending Big Data initiatives before they can provide any proof of positive returns.
The good news is that the volume of Big Data doesn’t have to be a deterrent. Instead, experiencing problems with increasingly large amounts of information can be a wake-up call for businesses to implement new technologies like a flexible storage and warehousing environment that are capable of scaling on-demand.
Enter: cloud computing.
Although the cloud has received a lot of attention in the application development, backup, and disaster recovery markets, its highly agile nature makes it an especially beneficial solution in the Big Data realm. By implementing a cloud storage architecture, for example, organizations can gather massive amounts of information without worrying about hitting capacity. And because the cloud is so scalable, decision-makers pay only for what they need when they need it, making the hosted environment ideal for the constantly changing demands of Big Data.
So what’s the catch?
There’s no doubt that cloud infrastructure services can be an appealing technology for companies looking to take advantage of the Big Data movement without encountering bandwidth or performance issues. However, that doesn’t mean the cloud is perfect. Some firms may encounter issues when using the cloud for the first time because the hosted services themselves are relatively new. The initial migration to the cloud, for example, can be difficult for enterprises that aren’t used to outsourcing or have never used managed services of any kind.