VerifyData 0.0.4

VerifyData is an intuitive library focused on data quality assessment, offering automated checks for completeness, consistency, and accuracy to guarantee high standards across your data flows.

VerifyData

VerifyData is a Python package for performing data quality checks on PySpark DataFrames. It provides a comprehensive set of checks to validate the integrity, consistency, and accuracy of data before processing, ensuring that data meets the required standards. The package is ideal for ETL workflows, data pipelines, and any scenario where high-quality data is essential.

Features

• Null Value Check: Ensures that specified columns do not contain null values.
• Uniqueness Check: Verifies that the values in a specified column are unique.
• Range Check: Checks if values in a column fall within a specified range.
• Valid Values Check: Confirms that values in a column belong to a list of predefined valid values.
• Schema Validation: Validates the presence of required columns and their data types.

Installation

To install DataQualityChecker, clone the repository and install dependencies:

pip install -r requirements.txt

Usage

Here’s how to use DataQualityChecker:

1. Initialize SparkSession:

   from pyspark.sql import SparkSession
   
   spark = SparkSession.builder.appName("DataQualityCheckerExample").getOrCreate()
   

2. Define DataFrame and Expected Schema: from pyspark.sql.types import StructType, StructField, IntegerType, StringType

   data = [(1, "John", 25, "M"), (2, "Jane", None, "F"), (3, "Alice", 35, None)]
   schema = StructType([
      StructField("id", IntegerType(), True),
      StructField("name", StringType(), True),
      StructField("age", IntegerType(), True),
      StructField("gender", StringType(), True)
    ])
   df = spark.createDataFrame(data, schema)
    
   expected_schema = {
      "id": IntegerType(),
      "name": StringType(),
      "age": IntegerType(),
      "gender": StringType()
    }
   

3. Initialize DataQualityChecker:

   from data_quality_checker import DataQualityChecker
   
   dq_checker = DataQualityChecker(df, expected_schema)
   

4. Run Checks:

   dq_checker.check_null_values()
   dq_checker.check_uniqueness("id")
   dq_checker.check_value_range("age", 20, 40)
   dq_checker.check_valid_values("gender", ["M", "F"])
   dq_checker.check_column_presence()
   dq_checker.check_column_data_types()
    
   results_df = dq_checker.run_checks()
   results_df.show(truncate=False)
   

Example Output

The output will be a DataFrame containing a summary of each check, with details on whether the check passed or failed:

Check	Passed	Details
Null check on column age	False	1 null value found
Uniqueness check on column id	True	All values are unique
Range check for column age	True	All values within range
Valid values check for column gender	False	1 invalid value found
Schema column presence check	True	All necessary columns are present
Data type check for column age	True	Data type matches

Running Tests

Run unit tests with unittest to verify the integrity of the DataQualityChecker:

python -m unittest discover