Big Data Management From Relational Database Management

With 3.2 billion internet users [1] and 6.4 billion internet-connected devices in 2016 alone [2], unprecedented amount of data is being generated and processed daily and increasingly every year. With the advent of web 2.0, the growth and creation of new and more complex types of data has created a natural demand for analysis of new data sources in order to gain knowledge. This new data volume and complexity is being called Big Data, famously characterised by Volume, Variety and Velocity and has created data management and processing challenges due to technological limitations, efficiency or cost to store and process in a timely fashion. The large volume and complexity of data cannot be handled and/or processed by most current information systems in a timely manner, while traditional data mining and analytics methods developed for a centralized data system may not be practical for Big Data. Tracking the concept of Big Data management from Relational Databases Management Systems to the current NoSQL database, this paper surveys the Big Data challenges from the perspective of its characteristics Volume, Variety and Velocity, and attempts to study how each of these challenges are addressed by various NoSQL systems. NoSQL is not a single system that can solve every single Big Data problem; it is an eco-system of technologies where different type of NoSQL databases are optimized to address various types of big data challenges by providing schema-less modeling and automaticShow MoreRelatedDefining Nosql Database And Its Classification Essay1160 Words   |  5 Pagesled database analysts and administrators to find more convenient ways to store the big amount of data. Big data is known as expression in the tech-world. It is defined as a huge collection of data that cannot be managed by relational databases (Moniruzzaman and Hossain 1). 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