The past few years have seen some of the most striking technological advances in recent memory. Of these, perhaps no two trends have had as much of an impact as the respective rises of cloud computing and big data analytics. Individually, each of these technologies can yield tremendous benefits for an organization. Combined, their power is even greater.
However, for a firm to fully leverage the potential capabilities of cloud computing and big data analytics, a number of challenges must be overcome. One of the most prominent of these is data migration. Without effective tools for moving data to, from and between cloud environments, cloud-based big data analytics cannot yield the desired benefits.
Big data in the cloud
Big data analytics' greatest value lies in its ability to produce valuable insight from unstructured and semistructured data sets that would otherwise be more of a burden than a benefit. This type of information is simply unusable without analytics technology. With these tools, however, firms can discover improved ways of running their internal operations, reaching out to customers and gaining an advantage over industry rivals.
Cloud computing's primary benefit is harder to pinpoint with certainty, but is likely its ability to improve information access. With the cloud, a company's employees can access and utilize corporate resources from home, remote locations or while traveling. As a result, companies leveraging the cloud become significantly more flexible than those lacking the technology. Workers are able to be more productive and often have higher morale.
Cloud-based big data analytics combines the best of these worlds. One of the biggest challenges a large company faces when it comes to big data analytics is ensuring that all necessary personnel, who are likely spread out over a number of locations, have access to up-to-date data sets. If this is not the case, the analytics produced by these workers will not be as beneficial or accurate as possible.
The potency of this combination can be seen in the rise of cloud-based big data storage options, most notably Amazon Web Services Redshift. This solution is aimed at providing companies with a platform for storing huge data sets in the cloud, and many industry experts, such as InfoWorld's David Linthicum, have predicted that the service will not only be a success, but lead to imitators in the near future.
By moving big data sets to the cloud, a company can ensure that all of its analytics workers have access to the necessary raw data at all times.
While this arrangement is ideal in theory, in practice there are several obstacles that firms must overcome. Most notably, most companies do not generate the majority of their big data in the cloud. On the contrary, this data is created and accumulated on-premises. Plans to utilize cloud-based big data analytics therefore require these firms to initially transfer big data to the cloud.
Yet companies utilizing legacy data migration solutions will likely struggle in this regard. These systems are not designed to meet the demands of big data, and as a result the company will experience delayed uploads and other inefficiencies. This will inevitably have a negative impact on the company's ability to leverage its cloud-based big data analytics technology.
Fortunately, solutions exist. By leveraging Attunity CloudBeam, businesses can easily and quickly upload data to RedShift and other cloud-based big data warehouses. This ensures that analytics specialists have access to the raw data they need regardless of location, allowing them to produce the most valuable, actionable analytics reports.