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Version: 1.7.0 (latest)

🧪 Data quality

dlt+

This page is for dlt+, which requires a license. Join our early access program for a trial license.

caution

🚧 This feature is under development. Interested in becoming an early tester? Join dlt+ early access.

dlt+ will allow you to define data validation rules at the YAML level or using Pydantic models. This ensures your data meets expected quality standards at the ingestion step.

Example: Defining a quality contract in YAML​

You can specify quality contracts to enforce constraints on your data, such as expected value ranges and nullability.

engine_version: 10
name: scd_type_3
tables:
  customers:
    columns:
      category:
        data_type: bigint
        nullable: false
        quality_contracts:
          expect_column_max_to_be_between:
            min_value: 1
            max_value: 100

Key features​

With dlt+, you will be able to:

  • Define data tests and quality contracts using YAML configuration or Pydantic models.
  • Apply both row-level and batch-level validation.
  • Enforce constraints on distributions, boundaries, and expected values.

Stay tuned for updates as we expand these capabilities! 🚀

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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