qbiz-data-raven 0.1rc1

A Python framework for building data quality tests

qbiz-data-raven

Description

A toolbox of flexible database connectors and test methods used to measure data integrity of datasets and database tables.

• Build data quality tests which can be inserted into an existing Python script or run as a stand-alone script.
• Send outcome notifications to messaging and logging applications.
• Halt pipelines and raise exceptions when needed.

Prerequisites

Python 3.6+

sqlalchemy>=1.3.19

psycopg2

pymysql

Installing

pip install qbiz-data-raven

A simple data quality test script

In this example we build a script to test the name, price and product_id columns from the Postgres table Orders. This table has the following DDL:

create table Orders (
id int,
name varchar(50),
order_ts varchar(26),
product_id int,
price float
);

Here's the test script.

import os

from qbizdataraven.connections import PostgresConnector
from qbizdataraven.data_quality_operators import SQLNullCheckOperator


def main():
    # initialize logging
    lazy_logger = lambda msg: print(msg + '\n')

    # database connection credentials
    user = os.environ["user"]
    password = os.environ["password"]
    host = os.environ["host"]
    dbname = os.environ["dbname"]
    port = os.environ["port"]

    # postgres database connector
    conn = PostgresConnector(user, password, host, dbname, port, logger=lazy_logger)
    dialect = "postgres"

    # test thresholds
    threshold0 = 0
    threshold1 = 0.01
    threshold5 = 0.05

    ##### TEST ORDERS TABLE #####
    # Table to be tested
    from_clause = "test_schema.Orders"

    # Conditional logic to be applied to input data
    date = "2020-09-08"
    where_clause = [f"date(order_ts) = '{date}'"]

    # Columns to be tested in target table
    columns = ("name", "product_id", "price")

    # Threshold value to be applied to each column
    threhold = {"name": threshold1, "product_id": threshold0, "price": threshold5}

    # Hard fail condition set on specific columns
    hard_fail = {"product_id": True}

    # Execute the null check test on each column in columns, on the above table
    SQLNullCheckOperator(conn, dialect, from_clause, threhold, *columns, where=where_clause, logger=lazy_logger,
                         hard_fail=hard_fail)


if __name__ == "__main__":
    main()

Documentation

Database Support

• Postgres
• MySQL

Data Quality Tests

Data quality tests are used to measure the integrity of specified columns within a table or document. Every data quality test will return 'test_pass' or 'test_fail' depending on the given measure and threshold.

Data Quality Operators

Each operator will log the test results using the function passed in the logger parameter. If no logger is found then these log messages will be swallowed.

Each operator has a test_results attribute which exposes the results from the underlying test. test_results is a dict object with the following structure:

{
    COLUMN NAME: {
        "result": 'test_pass' or 'test_fail',
        "measure": THE MEASURED VALUE OF COLUMN NAME,
        "threshold": THE THRESHOLD VALUE SPECIFIED FOR TEST,
        "result_msg": TEST RESULT MESSAGE
    }
}   

SQL Operators

All SQL operators have the following required parameters:

• conn - The database connection object.
• dialect - The SQL dialect for the given database. Accepted values are postgres or mysql.
• from_ - The schema and table name of table to be tested.
• threshold - The threshold specified for a given test or collection of tests. This parameter can be numeric or a dict object. If threshold is numeric then this value will be applied to all columns being tested by the operator. If threshold is a dict then each threshold value will be referenced by column name. All columns being passed to the operator must have a specified threshold value. If threshold is a dict it must have the following structure:

{
    COLUMN NAME: NUMERIC VALUE
}

• columns - The column names entered as comma separated positional arguments.

All SQL operators have the following optional parameters:

• logger - The logging function. If None is passed then logged messages will be swallowed.
• where - Conditional logic to be applied to table specified in from_.
• hard_fail - Specifies if an operator which has a test which results in 'test_fail' should terminate the current process. This parameter can be passed as a literal or a dict object. If hard_fail is set to True then every test being performed by the given operator which results in 'test_fail' will terminate the current process. If hard_fail is a dict object then each hard_fail value will be referenced by column name. Only those columns with a hard_fail value of True will terminate the process upon test failure. If hard_fail is a dict it must have the following structure:

{
    COLUMN NAME: BOOLEAN VALUE
}

• use_ansi - If true then compile measure query to ANSI standards.

SQLNullCheckOperator - Test the proportion of null values for each column contained in columns.

SQLDuplicateCheckOperator - Test the proportion of duplicate values for each column contained in columns.

SQLSetDuplicateCheckOperator - Test the number of duplicate values across all columns passed to the columns parameter simultaniously. This measure is equivalent to counting the number of rows returned from a SELECT DISTINCT on all columns and dividing by the total number of rows.

CSV Operators

All CSV operators have the following required parameters:

• from_ - The path to CSV file to be tested.
• threshold - Same as defined above for SQL operators.
• columns - the column names entered as comma separated positional arguments.

All CSV operators have the following optional parameters:

• delimiter - The delimiter used to separate values specified in the file refeneced by the from_ parameter.
• hard_fail - Same as defined above for SQL operators.
• fieldnames - A sequence of all column names for CSV file specified in from_ parameter. To be used if the specified file does not have column headers.
• reducer_kwargs - Key word arguments passed to the measure reducer function.

CSVNullCheckOperator - Test the proportion of NULL values for each column contained in columns.

CSVDuplicateCheckOperator - Test the proportion of duplicate values for each column contained in columns.

CSVSetDuplicateCheckOperator - Test the number of duplicate values across all columns passed to the columns parameter simultaniously.

Custom Operators

CustomSQLDQOperator - Executes the test passed by the custom_test parameter on each column contained in columns. The CustomSQLDQOperator class has the following required parameters:

• conn - The database connection object.
• custom_test - The SQL query to be executed. The custom_test query is required to return a column labeled result which takes value 'test_pass' or 'test_fail'. The custom_test query should also return columns measure, which provides the measured column value, and threshold, which gives the threshold used in the test. If these columns are present then these values will be logged and returned in the test_results attribute. If measure and threshold are not returned by the custom_test query then these values will be logged as None, and will be given in the test_results attribute as None. custom_test can also be a query template with placeholders {column} and {threshold} for variable column names and threshold values.
• description - The description of the data quality test being performed. The description is may contain placeholders {column} and {threshold} for the optional parameters columns and threshold, if they are passed to the CustomSQLDQOperator. In this case then a test description will be generated for each column in columns and for each value of threshold.

The CustomSQLDQOperator class has the following optional parameters:

• columns - a comma separated list of column arguments.
• threhsold - Same as defined above for SQL operators.
• hard_fail - Same as defined above for SQL operators.
• test_desc_kwargs - Key word arguments for formatting the test description.