Package: data-raven
data-ravenDescriptionA 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.PrerequisitesPython 3.6+sqlalchemy>=1.3.19psycopg2pymysqlInstallingpip install data-ravenA simple data quality test scriptIn this example we build a script to test thename,priceandproduct_idcolumns from the Postgres tableOrders.
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 dataraven.connections import PostgresConnector
from dataraven.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()DocumentationDatabase SupportPostgresMySQLData Quality TestsData 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 OperatorsEach operator will log the test results using the function passed in theloggerparameter. If no logger is found then
these log messages will be swallowed.Each operator has atest_resultsattribute which exposes the results from the underlying test.test_resultsis adictobject 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 OperatorsAll SQL operators have the following required parameters:conn- The database connection object.dialect- The SQL dialect for the given database. Accepted values arepostgresormysql.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 adictobject. Ifthresholdis numeric then this value will be applied to all columns being tested by the operator.
Ifthresholdis adictthen eachthresholdvalue will be referenced by column name. All columns being passed to the
operator must have a specified threshold value. Ifthresholdis adictit 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 infrom_.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 adictobject. Ifhard_failis set toTruethen every test being performed by the
given operator which results in'test_fail'will terminate the current process. Ifhard_failis adictobject then
eachhard_failvalue will be referenced by column name. Only those columns with ahard_failvalue ofTruewill
terminate the process upon test failure. Ifhard_failis adictit 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 incolumns.SQLDuplicateCheckOperator- Test the proportion of duplicate values for each column contained incolumns.SQLSetDuplicateCheckOperator- Test the number of duplicate values across all columns passed to thecolumnsparameter simultaniously. This measure is equivalent to counting the number of rows returned from aSELECT DISTINCTon
all columns and dividing by the total number of rows.CSV OperatorsAll 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 thefrom_parameter.hard_fail- Same as defined above for SQL operators.fieldnames- A sequence of all column names for CSV file specified infrom_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 ofNULLvalues for each column contained incolumns.CSVDuplicateCheckOperator- Test the proportion of duplicate values for each column contained incolumns.CSVSetDuplicateCheckOperator- Test the number of duplicate values across all columns passed to thecolumnsparameter simultaniously.Custom OperatorsCustomSQLDQOperator- Executes the test passed by thecustom_testparameter on each column contained incolumns.
TheCustomSQLDQOperatorclass has the following required parameters:conn- The database connection object.custom_test- The SQL query to be executed. Thecustom_testquery is required to return a column labeledresultwhich takes value'test_pass'or'test_fail'. Thecustom_testquery should also return columnsmeasure, which
provides the measured column value, andthreshold, which gives the threshold used in the test. If these columns are
present then these values will be logged and returned in thetest_resultsattribute. Ifmeasureandthresholdare
not returned by thecustom_testquery then these values will be logged asNone, and will be given in thetest_resultsattribute asNone.custom_testcan 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 parameterscolumnsandthreshold, if they are passed
to theCustomSQLDQOperator. In this case then a test description will be generated for eachcolumnincolumnsand
for each value ofthreshold.TheCustomSQLDQOperatorclass 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.