Cloud computing offers flexibility and efficiency at every stage in big data analytics – from accessing, analysing and delivering results. With a well defined data classification policy, it can be used wherever data privacy and security are the main concern.

Big data consists of both structured (transactional information, invoices, sales data) and unstructured data (emails, documents, video, images) generated by computers and the other connected devices. Streams of data are generated every day in an organisation, from business applications to data from various sensors and tracking devices. To process high volumes of data within a short period, big data requires a cluster of servers that comprise a huge amount of computing resources.

Every business today strives to find ways to gather, process and analyse perceptions from the different kinds of collected data. Organisations expect to derive valid perceptions from the collected data so that they can arrive at the most intelligent decision. Big data offers persistent efforts to arrive at a competitive solution that will benefit the organisation.

Normally, processing large data sets, which comprise both internal and external data, involve huge amount of computing power, expensive software licenses, manpower costs. Due to huge investment to procure anlaysis by big data, it can be out of reach for many organisations. But, the cloud computing tool can help organisations to achieve results in a cost effective manner.

Some of the familiar cloud service providers for big data and analytics services are: Amazon Web Services, RackSpace, Microsoft Azure, and Google. Microsoft Azure offers products that can analyse huge amount of data in real time and provide the ability to know how to increase revenue, to improve customer management and to maintain lower costs.

Azure HDInsight handles any amount of data on demand. The data can be from terabytes to petabytes and it gives the freedom to the customer to spin up any number of nodes at any time. It can process even semi-structured or unstructured data from devices, sensors, server logs, web clickstreams and social media. Analysing newly generated data, the user can explore and tap the relevant potential for new business opportunities.