Big data and cloud computing have been the new buzzwords that all of us by now must have either heard or read about. More and more IT organizations are looking forward to cloud computing as the best architecture to back their big data projects. Big data environments usually needs a network of servers to provision the tools that process the huge volumes, high speed, and various formats of data. While businesses often store their most confidential data in-house, massive volumes of data, including social media information may be sited externally. Evaluating the data where exactly it resides either in private or public clouds makes big data in the cloud more enticing in terms of cost and gaining speedy insights.

With the surge in the volume of unstructured data from social media networks, more value can be pulled out from big data when structured data sets are combined and evaluated to gain competitive edge. Definitely, the data is too big to process as well as move anywhere, so it's just the analytical program which needs to be shifted not the information. This can be possible only with public clouds, as most of the public data sets, including Facebook, Twitter, Pinterest, financial markets information, weather forecast data and accumulated industry-specific data reside in the cloud. And this taming of data in the cloud can be more economical for the organizations.

Let' have a closer look at the key drivers behind the increasing big data on cloud adoption:

Cost reduction: Cloud computing offers one of the most economical ways to support big data technologies and the cutting-edge analytics applications that can drive more business value. Organizations are now seeking ways to unravel data's veiled potential and deliver competitive advantage. Hence, in order to diligently mange data explosion IT organizations should consider embracing cloud computing to save costs through its pay-per-use model.

Instant provisioning/time to market: Provisioning of server has never been easy before. The emergence of cloud has made it a piece of cake for the users. Big data environments can now be easily scaled up or down easily based on the processing requirements. Faster provisioning is important for big data applications because the value of data reduces quickly as time goes by.

Flexibility/scalability: Big data analysis, specifically in the life sciences industry, has the need of enormous compute power for a short-term time period. In fact, for this type of analysis servers have to be provisioned within no time. This sort of scalability and elasticity can be realized in the cloud, substituting massive investments on next generation computers by paying for the computing on an hourly basis.

Conclusion: Cloud technology facilitates organizations with cost-effective and easy access to big data's huge volumes of information. Big data on the cloud engenders enormous magnitude of on-demand computing resources to carry out best practice analytics. And, both technologies are expected to persist to evolve and congregate in the coming time.