Data analytics is booming as an industry. The current age of technological evolution is such that terms like Big Data, Cloud Computing, Internet of Things and Artificial Intelligence have become wonted now. However, it is important to note that these technologies are connected to each other in a manner which may or may not be evident in the first go. With an enormous amount of data being processed using various mathematical and technological tools, there is an increasing demand for space and computational abilities that needs to be addressed in this space. Industries like retail, banking and finance, healthcare and telecom generate huge amounts of data every minute.
They thus, need to focus on how to deal with the 4 V’s of this data that form the foundation of modern Data Analytics: large volume of data, the high velocity of processing and transmitting data, wide variety of data that has to be processed and the varied veracity of the data coming in at every minute. Although the conventional data stream could be stored and processed in smaller database systems, with the boom in technologies like IoT, Big Data systems and Artificial Intelligence there is a growing need of bigger and more efficient data processing platforms. The need of the hour is to take data analytics to the cloud. Cloud Computing can expand the scope of processing abilities and the storage space of the data being run through processing algorithms.
Importance of Cloud Computing in Data Sciences
Big data has become a common scenario in all sectors. It calls for extremely high processing capacity which cannot be catered to, by conventional database systems. Owing to the previously mentioned high velocity and large volume of data, it does not follow the rules of conventional database management systems.
Moreover, this requires advanced algorithms and expertise to wrangle the data for maximum productivity. Lack of consistence in the data needs one to understand the problem deeply enough to perform analytics on it. The reiterative experimentation needs a smoother platform that can support seamless application of complex algorithms. The cloud as a platform facilitates a better processing atmosphere without adding a lot on to the costs.
Rapidly changing massive amounts of data also needs comparatively many more CPUs and memory resources, which can be built in the cloud infrastructure by establishing distributed processors and storage. Gaining critical business insights by querying data and analyzing such huge amounts of data is now expected to be done accurately and promptly (ideally in real-time). According to cloud-service providers, the elasticity of the cloud infrastructure thus, makes it ideal for big data analytics.
Moreover, hosting data and the processes in a data-center instead of an onsite-model helps you take advantage of the latest energy-efficient technology. Cloud service providers can also host multiple customers on shared infrastructure, thus driving higher and more efficient utilization of energy resources.
Additionally, with the diversification observed in the variety of data sources, there is a requirement for a cohesive platform that is compatible with all devices and processes. As per Gartner, there will be about 26 billion devices on the Internet of Things by 2020. The data being generated by this network of devices will predominantly be available on cloud. Therefore, flexibility, multiple processing systems and disparate data sets are highly recommended.
All the above and the core principles of data analytics are well entrenched with cloud computing. With the expansion of the scope of data analytics, there is a growing need of stringent security checks in data sciences. Cloud computing facilitates that while centralizing the data operations. One can follow strict regulations and compliance laws in the cloud without having to invest a lot of manual effort.
What may the caveats be?
Besides all the benefits of pulling data analytics and cloud computing together, there might be a few downsides too. For instance, the cloud’s distributed nature can be problematic for big data analysis. Robert Jenkins, co-founder and chief technology officer of CloudSigma, a Zurich-based Infrastructure as a Service (IaaS) provider, explains, “If you’re running Hadoop clusters and things like this, they put a really heavy load on storage, and in most clouds, the performance of the storage isn’t good enough.” He added to the explanation by saying that the big issue with clouds is to find a balance in the storage space with respect to the computing processes.
Some experts in the cloud-technology space also foresee minor hiccups in establishing the architecture needed for analyzing big data on the cloud. Henry Fastert, chief technologist and managing partner at SHI International, a large reseller, for instance, claims that devising an architecture that supports big data analysis in the cloud is no more daunting than meeting the challenges of satiating the rapidly growing appetite for cloud services in general. However, it is believed that none of these issues are insurmountable.
Having said that, processing data efficiently and shifting it to cloud organizations carries two benefits viz. tackling large sets of data for decision making and reducing the overall cost incurred in setting the infrastructure up. With a huge demand in both the fields and billions of dollars invested, both are here to stay.