Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in big data environments. Techniques used for big data are machine learning, Data Mining, Neural Network and Deep Learning. There are big data architecture offerings from Microsoft, IBM and National Institute of Standards and Technology. Big data potentials can transform economies and reduce running cost of institutions. Big data has challenges such as storage, computation, security and privacy
Qd-tree: Learning Data Layouts for Big Data Analytics