In your approach to transformations, do you use frameworks like Spark‑Testing‑Base or do you mock the Spark context?

For testing Spark transformations, we prefer using a lightweight testing framework such as spark‑testing‑base. It provides a local SparkSession that runs in memory, allowing us to write unit tests that assert DataFrame equality without spinning up a full cluster. This approach is faster and easier to integrate into CI pipelines. In cases where we need to test interactions with external services (e.g., Hive, HDFS), we mock the SparkContext or use a local Hadoop mini‑cluster. Mocking is useful for isolating the transformation logic, but it can miss integration issues that only surface in a real cluster. Therefore, we combine unit tests with a small integration test that runs the job on a Dockerized Spark cluster to catch any runtime or configuration problems.