Test

An Overview

Tests in Ascend are validation mechanisms applied to data components, such as Read Components, Transforms, and more, to ensure the data meets specific quality and integrity criteria. These tests can range from simple checks, like verifying non-null values in a column, to more complex validations, such as ensuring data consistency across different datasets.

Key Features of Tests

1. Versatility: Ascend supports a wide array of tests, enabling validation at both the column level and the component level. This versatility allows users to enforce data quality and integrity checks that are tailored to their specific needs.
2. Customization: Beyond built-in tests, Ascend allows for the creation of custom tests, both in SQL and Python, offering the flexibility to define unique validation logic.
3. Inline Validation: Tests in Ascend can be defined inline with the data processing components, allowing for immediate and context-aware validation.
4. Automation: Tests are automatically applied during data processing, providing continuous assurance of data quality without manual intervention.

How Tests Work

Tests in Ascend are defined within the configuration of data processing components. When data flows through these components, the defined tests are executed against the data, validating it against the specified criteria. If a test fails, the platform can halt processing and alert users, ensuring that data quality issues are addressed promptly.

Types of Tests in Ascend

• Column-Level Tests: Target specific columns within a dataset, useful for validating data types, uniqueness, range constraints, and more.
• Component-Level Tests: Apply to the entire output of a component, ensuring overall data structure, consistency, and accuracy.
• Standalone Tests: Defined independently of specific components, these tests are reusable and can validate data across multiple tables or components. They're particularly useful for cross-dataset validations or when applying a common set of standards across different parts of the pipeline.

Built-in and Custom Tests

• Built-in Tests: A suite of predefined tests covering common validation scenarios, such as checking for null values, ensuring column uniqueness, verifying value ranges, and more.
• Custom Tests: Allow users to define specific validation logic not covered by built-in tests. These can be defined using SQL or Python, depending on the complexity and requirements of the validation.

Best Practices for Implementing Tests

• Comprehensive Testing: Implement a broad range of tests to cover all critical aspects of data quality and integrity. This includes both built-in tests for common validations and custom tests for specific use cases.
• Regular Review and Adjustment: Continuously review and adjust tests as data schemas and business requirements evolve to ensure ongoing relevance and effectiveness.
• Performance Considerations: While thorough testing is essential, be mindful of the performance implications. Optimize custom tests to minimize their impact on data processing times.

Conclusion

Tests are an integral part of Ascend's data engineering platform, ensuring that data pipelines produce reliable, high-quality data. By leveraging both built-in and custom tests, users can enforce strict data quality standards, automating the validation process and significantly reducing the risk of data errors. Understanding how to effectively implement and utilize tests is crucial for maintaining the integrity and accuracy of data within Ascend's ecosystem.