5. Transform

5.1. Introduction

Once data has been extracted from a DatabaseSource or CallableSource, it can be cleaned and expanded upon by ‘expansions’.

Conceptually, pylytics is a pipeline from extract to load.

Each row which comes from the source is passed through this pipeline.

The pipeline consists of a number of expansions, which transform the data. At the end of the pipeline, the data is ready to the loaded.

The expansions are simple, testable functions. For example:

# transform.py

def convert_datetime_to_date(data):
    """ We're only interested in the created date, and not the time.
    data['created_date'] = data['created_datetime'].date()
    del data['created_datetime']

def convert_str_to_int(data):
    """ The source returns integers as strings - convert them.
    data['sales'] = int(data['sales'])

The data argument is a dictionary representing a single row.

5.2. Adding expansions to facts and dimensions

For example:

# load.py

from transform import convert_datetime_to_date, convert_str_to_int

class Sales(Fact):

    __source__ = DatabaseSource.define(
        query="SELECT * FROM sales_table",
        expansions=[convert_datetime_to_date, convert_str_to_int]


Both DatabaseSource and CallableSource accept the expansions argument.

The expansions are processed in the order they appear in the list.