Parquet Compression in Windows with Big data-An Enhanced Storage Style

The paper focusses on highlighting compression utilities/applications used in generic operating systems and enhancing those using a Big Data Architecture-based engine. Storage is a compelling factor in commonly used machines and using such techniques, low storage capacity issues in machines can be reduced and hence efficiency will be improved. The focus is on creating a proof of concept that such an efficient compression type can also be used on files on an operating system like windows and not only in the big data infrastructure and hence going on ahead with its uses and enhancements. Research work also aims at achieving compression of the files into parquet files into Windows which is not being done currently.