  Big data sets must be carefully partitioned into statistically similar data
subsets that can be used as representative samples for big data analysis tasks.
In this paper, we propose the random sample partition (RSP) data model to
represent a big data set as a set of non-overlapping data subsets, called RSP
data blocks, where each RSP data block has a probability distribution similar
to the whole big data set. Under this data model, efficient block level
sampling is used to randomly select RSP data blocks, replacing expensive record
level sampling to select sample data from a big distributed data set on a
computing cluster. We show how RSP data blocks can be employed to estimate
statistics of a big data set and build models which are equivalent to those
built from the whole big data set. In this approach, analysis of a big data set
becomes analysis of few RSP data blocks which have been generated in advance on
the computing cluster. Therefore, the new method for data analysis based on RSP
data blocks is scalable to big data.
