vinod901 / soda-core

:zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io

Soda Core

Data quality testing for SQL-, Spark-, and Pandas-accessible data.

✔ An open-source, CLI tool and Python library for data quality testing
✔ Compatible with the Soda Checks Language (SodaCL)
✔ Enables data quality testing both in and out of your data pipelines and development workflows
✔ Integrated to allow a Soda scan in a data pipeline, or programmatic scans on a time-based schedule

Soda Core is a free, open-source, command-line tool and Python library that enables you to use the Soda Checks Language to turn user-defined input into aggregated SQL queries.

When it runs a scan on a dataset, Soda Core executes the checks to find invalid, missing, or unexpected data. When your Soda Checks fail, they surface the data that you defined as bad-quality.

Soda Library

Consider using Soda Library, an extension of Soda Core that offers more features and functionality, and enables you to connect to a Soda Cloud account to collaborate with your team on data quality. Install Soda Library and get started with a 45-day free trial.

Get started

Soda Core currently supports connections to several data sources. See Compatibility for a complete list.

Requirements

• Python 3.8 or greater
• Pip 21.0 or greater

Install and run

1. To get started, use the install command, replacing soda-core-postgres with the package that matches your data source. See Install Soda Core for a complete list.
   

   pip install soda-core-postgres

2. Prepare a configuration.yml file to connect to your data source. Then, write data quality checks in a checks.yml file. See Configure Soda Core.

3. Run a scan to review checks that passed, failed, or warned during a scan. See Run a Soda Core scan.

   soda scan -d your_datasource -c configuration.yml checks.yml

Example checks

# Checks for basic validations
checks for dim_customer:
  - row_count between 10 and 1000
  - missing_count(birth_date) = 0
  - invalid_percent(phone) < 1 %:
      valid format: phone number
  - invalid_count(number_cars_owned) = 0:
      valid min: 1
      valid max: 6
  - duplicate_count(phone) = 0

# Checks for schema changes
checks for dim_product:
  - schema:
      name: Find forbidden, missing, or wrong type
      warn:
        when required column missing: [dealer_price, list_price]
        when forbidden column present: [credit_card]
        when wrong column type:
          standard_cost: money
      fail:
        when forbidden column present: [pii*]
        when wrong column index:
          model_name: 22
# Check for freshness 
  - freshness(start_date) < 1d

# Check for referential integrity
checks for dim_department_group:
  - values in (department_group_name) must exist in dim_employee (department_name)

Documentation

• Soda Core
• Soda Checks Language (SodaCL)