Big Data

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."


While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have a strong expertise in statistics, are called data scientists.

No, data analytics is a general term for any type of processing that looks at historical data over time, but as the size of organizational data grows, the term data analytics is evolving to favor big data-capable systems.

The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively.

While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.

Flexibility : Only pay for the resources you need. Scale up and down the capacity as per your requirements.

Less Work Load on IT Staff : Offloading tasks related to server maintenance, security and issues to the established cloud vendors.

Easy Deployment : See your creative ideas into actions. Cloud makes developing and deploying apps easy and robust.

Easy Access : Makes the resource sharing easy. Enables enterprise mobility by giving employees the benefit of “work-from-any-part-of-the-world”.

Bigdata Models

Web application development

SaaS (Software as a Service)

Simple and straightforward pay-for-subscription model. Cloud-based apps run in the cloud and can be accessed by users via Internet.
Ex : Dropbox

PaaS (Platform as a Service)

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.

IaaS (Infrastructure as a Service)

Provides you the computing infrastructure, physical or (quite often) virtual machines on pay-per-use basis.