In the current corporate environment, two of the most impactful trends include the use of cloud systems to make items accessible, and the utilization of big data analytics to illuminate new insights. A considerable number of organizations leverage one strategy or the other, and some even have both approaches operating within their business. When the cloud is used to power big data analytics initiatives, companies benefit that much more.
Big data analytics advantages offered by the cloud
By now, the perks that the cloud can provide for typical corporate practices have been well communicated. But what can cloud technology offer for big data projects?
One of the biggest advantages brought by the cloud is its inherent scalability, and this potential for growth is a major asset when it comes to big data initiatives. Wired contributor Ashish Thusoo pointed out that by its own nature, big data requires room to house the vast sums of information that make it up. And as the business leverages this content more frequently and for additional purposes, it will need space to grow.
"While traditional solutions would require the addition of more physical servers to the cluster in order to increase processing power and storage space, the virtual nature of the cloud allows for seemingly unlimited resources on demand," Thusoo wrote.
This way, the company's cloud can easily be scaled alongside its big data, ensuring that there is always space for the collection and analysis of important information.
As an added bonus, the firm can save operating expenses by cost-efficiently growing its cloud instead of spending unnecessary capital on hardware. This is another advantage the cloud can provide when it comes to big data analytics. Thusoo noted that in the past, organizations would invest untold sums in their hardware infrastructure just to guarantee they had enough processing power and storage space on hand. This strategy often lead to overspending on these resources, which is all but eliminated when the cloud is introduced into the project.
Yet another perk of combining the power of the cloud within a big data analytics initiative is the technology's speed and uptime. Within a traditional on-premise hardware operation, it can take days or even weeks to add a new server and get it up and running. This process could negatively impact the timeline of a big data project as staff members wait for the IT team to get additional resources on hand and operating as they should. The cloud, on the other hand, can be deployed or scaled in mere minutes, ensuring that the project stays on schedule even as a new system is being installed.
What data should be moved to the cloud?
Although the cloud can clearly bring myriad benefits to big data analytics, there are certain situations when moving this information into a virtual environment is even more advantageous. One instance includes the use of enterprise applications, InfoWorld contributor James Kobielus noted.
Oftentimes, businesses already house and operate these resources through the cloud, but many may not have realized that this type of utilization could be leveraged for big data.
"If, like many organizations – especially small and midmarket businesses – you use cloud-based applications from an external service provider, much of your source transactional data is already in a public cloud," Kobielus pointed out. "If you have deep historical data on that cloud platform, it might already have accumulated in big data magnitudes."
As this content is already in the cloud, it can be even more beneficial when it is gathered into clusters and analyzed for big data insights. This approach offers a range of different use cases, as depending on the type of application being used, the company will have a specific type of data at its fingertips.
Kobielus also noted that when an organization is utilizing high-volume external data sources for a certain project – like an initiative involving customer sentiment monitoring, for example – it may not have the necessary resources on hand to house and process this information. With countless pieces of data connected with each individual client, the business would need a considerable amount of servers, storage capacity and bandwidth on hand to leverage the content for big data analytics. Placing this information in the cloud, however, eliminates this need as the system provides these critical resources in a scalable manner.
Whereas these high-volume external information sources may be leveraged over a long period of time for several different projects, the corporation may also have significantly large, but short lived data sandboxes that it needs to house as well. Kobielus noted that this is also a perfect time to utilize the cloud. This content can be easily migrated to the cloud for the small window of time that it is needed, then removed after it has been processed and analyzed. And thanks to the flexible nature of the cloud, the system can be scaled up to account for these sandboxes, then scaled back down after they have been utilized.
Overall, there are a considerable number of use cases that show the power of the cloud as it connects to big data analytics. And since neither technology trend seems to be going anywhere anytime soon, it is quite likely that the business sector will see many more of these types of initiatives to come in the near future.
"As the technology becomes more affordable and accessible to enterprises in a variety of industries, the benefits of cloud-based big data analytics will become increasingly apparent as more and more businesses get on the cloud," Thusoo wrote.