  In the era of Big Code, when researchers seek to study an increasingly large
number of repositories to support their findings, the data processing stage may
require manipulating millions and more of records.
  In this work we focus on studies involving fine-grained AST level source code
changes. We present how we extended the CodeDistillery source code mining
framework with data manipulation capabilities, aimed to alleviate the
processing of large datasets of fine grained source code changes. The
capabilities we have introduced allow researchers to highly automate their
repository mining process and streamline the data acquisition and processing
phases. These capabilities have been successfully used to conduct a number of
studies, in the course of which dozens of millions of fine-grained source code
changes have been processed.
