A dominant combination of Big Data and Cloud Computing

Big Data on the cloud, and for good reasons: from aiding the discovery of new drugs  to predicting weather forecasts, earthquakes. Big Data analytics and the cloud are proving to be a dominant combination. Today scenario Cloud computing is a business enabler having a potential to take the business to the next level.

For any business, data is the most bases, for any transformation in the business; therefore, managing proliferating data is crucial.

Most of the big data projects are processed in the public cloud. Big data is required to be scaled on distributed cluster as per the demand and requirements.  The biggest factor to process big data is data gravity and data elasticity. Cloud has a big role to play to get this done.

There seems to be a dire need for the adoption of big data over cloud such that the business gets benefited with instant reporting and analytical requirements. The main reasons for efficiently managing structured, unstructured and semi-structured data of varying sizes over cloud are as follows:

Excellent bi-directional Scalability (Vertical & Horizontal)

As per the definition, Elasticity in Big Data analytics calls for innovative processing and volume requirements, in order to meet the 3V property of the big data, namely velocity, veracity and volume of the data, necessitates additional infrastructure. Additionally, the demand for processing power is not uniform, fluctuating at different times of the year. 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. With the cloud, enterprises can scale up or down to the desired level of processing power and storage space easily and quickly.

Potential ability, power, and Capability

At this age of data explosion, today’s companies are processing 1,000 times more data than they did only a decade ago. With the proliferation of social media, 80 percent of the world's data is unstructured, and unorganized tweets, likes, videos, photos, blogs such data cannot be analyzed by traditional methods. Big Data platforms like Apache Hadoop have the capability to analyze all available 3V data. And the cloud makes the whole process easier and more accessible to both large and small enterprises.

Inexpensive and Affordable

One of the benefits of cloud computing is pay-as–you-use i.e., pay for the resources company need to store and process Big Data. In the pre-cloud era, businesses had to invest large sums of capital to purchase the necessary hardware. In order to allow for future data needs, companies typically overspent, buying more hardware than they actually required for accomplishing the task at hand. With the advent of cloud-based computing, companies can choose between hosting expensive on-site servers---which may need to be managed by IT teams or simply purchasing scalable space on demand and only paying for the storage space and processing power they actually use.

 Momentum and mobility (2M) to sustain Speed and Agility

Enterprises with traditional infrastructure used to get a new server up and running. But the real costs of time delays lies in interrupted innovation. Cloud-based services allow companies to provision whatever resources they need---as they need them. In fact, a cloud database allows hundreds and even thousands of virtual servers to be deployed smoothly and seamlessly in minutes.

The era of Big Data has arrived. And cloud capabilities are taking Big Data analytics to a new level. As the technology is 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.

The Divergent types of Big Data

In a pursuit to the software industry, big data refers to those data sets that exceed the capabilities of traditional databases. Big data is a kind of collection of divergent data, should be able to adapt to intelligence.  For many big data users, the definition of big data is an acronym for predictive analytics. For few others, the definition of big data is just an impressive amount of 1s and 0s.

The term ‘Big Data' is too general. The few different categories of Data today, are listed below:

Big Data : Such data are the classic predictive analytics problems where you want to unearth trends or push the boundaries of scientific knowledge by the mining of complex huge amount of data. A typical human genome scan generates about 200GB of data and the number of human genomes scanned is doubling every seven months, according to a study conducted by the University of Illinois (And we're not even counting the data from higher-level analyses or the genome scans from the 2.5 million plants and animal species that will be sequenced by then.) By 2025, we will have 40 exabytes of human genomic data or about 400 times more than the 100 petabytes now stored in YouTube. In general, larger the data sets, precise will be the conclusions. Still, the vast scope means rethinking where and how data gets stored and shared.

Fast Data : To seize the velocity of data in real-time is among the most important challenges of the big data. Compute of complex mathematical analytics enhances the accuracy in predicting the data at real-time. Every information or data is expected to process on a figure tip as a FAST data. Business can quickly analyze a consumer's personal preferences as they pause by a store kiosk and dynamically generate a 10% off coupon. Fast Data sets can be high in volume, but the value revolves around being able to deliver it on time. All availability of data, in real time, is generating the need to keep pace with the retrieval and process of the information. Some important data has to be forecasted immediately in real time, for example, the status of vital parameters of a patient at ICU, prediction of weather forecast, data from crucial sensors, an accurate traffic forecast in real-time than a perfect analysis an hour before, mandatory data from installed cameras at railways or airport, to detect telltale signals of intoxication to keep people away from falling onto the tracks or at airbase, etc. Big players of these fields like IBM and Cisco are building and designing their systems keeping these multifaceted properties of data in mind.