Artificial Intelligence (AI), mobile, social and Internet of Things (IoT) are driving data complexity, new forms and sources of data.
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data from different sources and in different sizes from terabytes to zettabytes.
Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety.
Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale. Big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable.
"Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independently or together with their existing enterprise data to gain new insights resulting in better and faster decisions."
Simple and straightforward pay-for-subscription model. Cloud-based apps run in the cloud and can be accessed by users via Internet.
Ex : Dropbox
No need of buying and managing hardware and software to develop and operate your app. The framework, OS, and hardware are in the cloud for the app’s entire life-cycle.
Provides you the computing infrastructure, physical or (quite often) virtual machines on pay-per-use basis.