Data Quality Frameworks Comparison

This blog post will compare some of the top open-source data quality tools: Deequ, dbt Core, MobyDQ, Great Expectations, Soda Core, and Cucumber.

1. Deequ

Features:

• Developed by Amazon, focuses on data quality for big data.
• Provides a suite of metrics for data profiling and quality checks.
• Integrates well with Apache Spark.

Use Cases:

• Big Data Analytics: Ideal for ensuring data quality in large-scale data processing environments.
• Data Profiling: Suitable for profiling large datasets to understand their characteristics and quality.

Example:

from pyspark.sql import SparkSession
from pydeequ.analyzers import AnalysisRunner, AnalyzerContext
from pydeequ.checks import Check, CheckLevel
from pydeequ.verification import VerificationSuite

spark = SparkSession.builder.appName("DeequExample").getOrCreate()
df = spark.read.csv('your_data.csv', header=True, inferSchema=True)

analysis_result = AnalysisRunner(spark).onData(df).addAnalyzer(Size()).addAnalyzer(Completeness("important_column")).run()
analysis_result_df = AnalyzerContext.successMetricsAsDataFrame(spark, analysis_result)
analysis_result_df.show()

2. dbt Core

Features:

• Open-source data transformation tool.
• Supports data testing and validation through built-in tests.
• Integrates with various data warehouses and databases.

Use Cases:

• Data Transformation: Ideal for transforming raw data into a format suitable for analysis.
• Data Validation: Suitable for validating data during the transformation process.

Example:

# dbt_project.yml
name: 'my_project'
version: '1.0'

# models/schema.yml
version: 2
models:
  - name: my_model
    columns:
      - name: important_column
        tests:
          - not_null

3. MobyDQ

Features:

• Open-source data quality tool.
• Provides a suite of metrics for data profiling and quality checks.
• Supports automated data quality monitoring and reporting.

Use Cases:

• Data Profiling: Suitable for profiling datasets to understand their characteristics and quality.
• Data Quality Monitoring: Ideal for automated monitoring of data quality metrics.

Example:

import mobydq

# Load data
df = mobydq.load_data('your_data.csv')

# Profile data
profile = mobydq.profile_data(df)

# Display profile
mobydq.display_profile(profile)

4. Great Expectations

Features:

• Allows you to define and validate data quality expectations.
• Supports automated testing and monitoring of data pipelines.
• Provides a user-friendly interface for managing expectations.

Use Cases:

• Data Pipeline Monitoring: Perfect for ensuring data quality in automated data pipelines.
• Data Validation: Suitable for validating data against predefined expectations before it enters production systems.

Example:

import great_expectations as ge

context = ge.data_context.DataContext()
df = pd.read_csv('your_data.csv')
expectation_suite = context.create_expectation_suite(expectation_suite_name="my_suite")
validator = context.get_validator(batch_request=df, expectation_suite=expectation_suite)
validator.expect_column_values_to_not_be_null("important_column")
validator.save_expectation_suite(discard_failed_expectations=False)

5. Soda Core

Features:

• Open-source data quality tool.
• Supports SQL-based data quality checks.
• Provides a simple and intuitive syntax for defining checks.

Use Cases:

• Data Warehousing: Perfect for ensuring data quality in data warehouses and business intelligence systems.
• SQL-Based Data Checks: Suitable for organizations that prefer SQL for data quality checks.

Example:

import soda.core

soda_client = soda.core.SodaClient()
scan = soda_client.scan_yaml(
    data_source_name="your_data_source",
    scan_definition_name="your_scan_definition",
    checks_yaml="""
    checks for df:
      - row_count > 0:
          name: Row count should be greater than 0
      - missing_count(important_column) = 0:
          name: No missing values in important_column
    """,
)
scan.execute()

6. Cucumber

Features:

• Open-source testing framework.
• Supports behavior-driven development (BDD) for data quality testing.
• Provides a human-readable syntax for defining tests.

Use Cases:

• Behavior-Driven Development: Ideal for defining and testing data quality expectations in a human-readable format.
• Automated Testing: Suitable for automated testing of data quality scenarios.

Example:

# features/data_quality.feature
Feature: Data Quality Checks

  Scenario: Validate data completeness
    Given the data is loaded
    When I check for missing values in "important_column"
    Then there should be no missing values

Comparison of Open Source Data Quality Tools