faker-data-generation

You can generate synthetic data with intentional data quality issues by using Faker to create realistic datasets and applying a configurable corruption rate to introduce specific violations like missing values or invalid formats. This tests your pipeline's robustness against edge cases.

The best way to create realistic test data for a Bronze layer is to use a Faker-based generation process that produces synthetic records mimicking production scenarios. It supports configurable distributions and patterns tailored to your specific data architecture.

Yes, you can map synthetic data corruption patterns to specific DQ expectations. The generation process introduces intentional data quality violations that align with DLT expectations, allowing you to validate data quality rules effectively within your pipeline.

Faker data generation supports configurable distributions for synthetic datasets. By leveraging numpy and pandas dependencies, it creates realistic data patterns that mimic production scenarios for comprehensive testing and development.

You introduce missing values and invalid formats by configuring the corruption rate during synthetic data generation. This process injects specific data quality violations into the Faker-generated records, creating edge cases to test your validation logic.

Synthetic data generation should be used instead of production data to avoid exposing sensitive information while still testing data quality validation. It allows you to intentionally inject controlled corruption patterns and edge cases that production data may not naturally exhibit.