I didn't catch that. Can you tell me about the testing frameworks you use for data transformations in your CI/CD pipeline?

In a CI/CD pipeline for data transformations, we typically layer tests to catch issues early. First, unit tests validate individual transformation functions using frameworks like pytest or unittest in Python, or ScalaTest for Spark UDFs. Next, integration tests run the full ETL job against a small, representative dataset in a local or Dockerized Spark cluster; libraries such as spark‑testing‑base or spark‑testing‑framework help mock a SparkSession and assert DataFrame results. For data‑quality checks, Great Expectations or dbt tests are common; they allow you to declare expectations (e.g., no nulls in a key column, value ranges) and run them as part of the pipeline. These tests are wired into the CI/CD system (GitHub Actions, Jenkins, GitLab CI) so that a failed test aborts the deployment. Finally, end‑to‑end tests may compare the output of the pipeline against a known good snapshot or a downstream system. By combining unit, integration, and data‑quality tests, we ensure that transformations are correct, performant, and produce valid data before they reach production.