## Parse a CSV file with Python

For this task, I leveraged my experience in web software development, particularly with Python, to quickly create a script capable of downloading the requested CSV file and parse it into a list of dictionaries. This functionality exists in the `download_csv` function, and can be easily run from a Python interactive session with an arbitrary URL. Simply import the script file and call this function. Note that it assumes that the first line of the downloaded CSV file contains column headers. This is how it generates dictionaries from each line, and the given CSV file is in this format.

From here, the data can be easily manipulated and searched. When the script is run from the command line, it will produce the requested report, counting active and inactive users, and validating the email, role, status, and created date for eeach user. Alternatively, the `main` function can be called from an interactive session after importing the file.

For an exceptionally large CSV, this might become slow to search through, as it is just a native Python list. For crazy-large datasets (such as CSV-format log files spanning months or years), or when relating between multiple CSV files (like in a database), it would probably make more sense to use something like Pandas rather than the `csv` builtin library, as it makes use of the more efficient `numpy` arrays and can do indexing.