Working Efficiently with Large SAS® Datasets

Many SAS users experience challenges when working with large SAS datasets having millions of rows, hundreds of columns and size close to a gigabyte or even more. Often it takes enormous time to process these datasets which can have an impact on delivery timelines. Also, storing such datasets may cause shortage of permanent storage space or can exhaust the available memory. In order to handle these constraints, one can think of making a large dataset smaller by reducing the number of observations and/or variables or by reducing the size of the variables without losing any of its information. This paper will focus on techniques supported by test results to reduce the size of huge datasets and work with them efficiently by striking a balance between the processing time and resource limitations. Some of the efficiency techniques described in this paper can also be applied while working with smaller datasets.