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BigData and Hadoop

Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy. There are three dimensions to big data known as Volume, Variety and Velocity.

Apache Hadoop is an open-source software framework used for distributed storage and processing of dataset of big data using the MapReduce programming model. It consists of computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.

Big data can be described by the following characteristics:
Volume The quantity of generated and stored data. The size of the data determines the value and potential insight- and whether it can actually be considered big data or not.
Variety The type and nature of the data. This helps people who analyze it to effectively use the resulting insight.
Velocity In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
Variability Inconsistency of the data set can hamper processes to handle and manage it.
Veracity The data quality of captured data can vary greatly, affecting the accurate analysis.


Intranet applications allow organisations to control a diverse range of information efficiently, and most importantly communicate, manage and share this information between relevant staff.



An ERP system supports most of the business system that maintains in a single database the data needed for a variety of business functions such as Manufacturing, Supply Chain Management, Financials, Projects, Human Resources and Customer Relationship Management.