  Background: The need for big data analysis requires being able to process
large data which are being held fine-tuned for usage by corporate. It is only
very recently that the need for big data has caught attention for low budget
corporate groups and academia who typically do not have money and resources to
buy expensive licenses of big data analysis platforms such as SAS. The
corporate continue to work on SAS data format largely because of systemic
organizational history and that the prior codes have been built on them. The
data-providers continue to thus provide data in SAS formats. Acute sudden need
has arisen because of this gap of data being in SAS format and the coders not
having a SAS expertise or training background as the economic and inertial
forces acting of having shaped these two class of people have been different.
Method: We analyze the differences and thus the need for SasCsvToolkit which
helps to generate a CSV file for a SAS format data so that the data scientist
can then make use of his skills in other tools that can process CSVs such as R,
SPSS, or even Microsoft Excel. At the same time, it also provides conversion of
CSV files to SAS format. Apart from this, a SAS database programmer always
struggles in finding the right method to do a database search, exact match,
substring match, except condition, filters, unique values, table joins and data
mining for which the toolbox also provides template scripts to modify and use
from command line. Results: The toolkit has been implemented on SLURM scheduler
platform as a `bag-of-tasks` algorithm for parallel and distributed workflow
though serial version has also been incorporated.
