SQL is an essential part of data-driven applications, powering everything from simple queries to complex data transformations. However, ensuring the accuracy and reliability of SQL code is often challenging, particularly when dealing with intricate logic or large-scale datasets. Also, deploying changes in SQL code to production is another complex task, as it requires careful validation to avoid breaking the query logic.

Fortunately, integrating Python’s testing framework such as pytest into SQL workflows provides a streamlined solution for these challenges. Such approach enables creating clean, efficient, and automated testing processes for SQL code and database logic. Therefore, we can validate query results, enforce schema consistency, and simulate complex data scenarios, all while reducing manual effort and improving test coverage.

This talk will address:

• configuring lightweight database fixtures
• verifying SQL query result and testing scripts seamlessly
• data mocking
• schema validation
• testing non-deterministic queries
• handling large datasets

Attendees will gain insights into improving SQL code quality, identifying issues early in the development process, and ensuring the reliability of data-driven products. This presentation is particularly beneficial for Data Scientists, Engineers, and Analysts seeking to enhance the efficiency and precision of their testing practices.