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Background: The need for big data analysis requires being able to processlarge data which are being held fine-tuned for usage by corporate. It is onlyvery recently that the need for big data has caught attention for low budgetcorporate groups and academia who typically do not have money and resources tobuy expensive licenses of big data analysis platforms such as SAS. Thecorporate continue to work on SAS data format largely because of systemicorganizational history and that the prior codes have been built on them. Thedata-providers continue to thus provide data in SAS formats. Acute sudden needhas arisen because of this gap of data being in SAS format and the coders nothaving a SAS expertise or training background as the economic and inertialforces acting of having shaped these two class of people have been different.Method: We analyze the differences and thus the need for SasCsvToolkit whichhelps to generate a CSV file for a SAS format data so that the data scientistcan 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 ofCSV files to SAS format. Apart from this, a SAS database programmer alwaysstruggles in finding the right method to do a database search, exact match,substring match, except condition, filters, unique values, table joins and datamining for which the toolbox also provides template scripts to modify and usefrom command line. Results: The toolkit has been implemented on SLURM schedulerplatform as a `bag-of-tasks` algorithm for parallel and distributed workflowthough serial version has also been incorporated.

SasCsvToolkit -- A versatile parallel 'bag-of-tasks' job submission  application on heterogeneous and homogeneous platforms for Big Data Analytics  such as for Biomedical Informatics