Every day companies demand to capture more data and process them quickly in a simplified way and with economic hardware resources, for it arises the area of knowledge known as Big Data and very closely related to this, new forms of data storage based on NoSQL systems. The educational sector is not exempt from these needs and demands, so it is necessary to have an infrastructure that facilitates the processing of Massive Educational Data.
Based on these elements: What kind of NoSQL database do you recommend for determinate educational data types?
Educational data is not just humungous data but also heterogeneous data. Traditional data mining approaches are applied to obtain patterns and move towards predication. But mining of educational data tends to focus on development of new tools to determine patterns and apply techniques to analyse large data sets. Due to the sheer volume and variety associated with Big Data the traditional approaches might fall short for deliverance. The big storage requirement is also one of the major concerns to be handled. Big Data is usually associated with no-schema, high scalability. NoSQL data stores like Cassandara, MongoDB, CouchDB try to meet the requirements of “Big Data”. NoSQL databases are inherently schema less and highly scalable. These databases support parallel processing of the large amount of data. MapReduce is a data processing paradigm for squeezing the large volumes of data into useful and comprehensive results.
Thanks @Kgs Venkatesan for your contribution, that is very interesting and useful to use a MapReduce approach for processing a huge amount educational data and get comprehensive results.
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