Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
CI/CD for PySpark data pipelines
·
Module 1
Testing Fundamentals for PySpark ETL
1
Automated Testing for Data Pipeline Reliability
Explain the role of automated testing in data pipeline reliability.
Structuring PySpark ETL Projects
Organize a PySpark ETL project directory to separate source code, tests, and configuration.
Writing Basic Pytest Assertions
Write a basic pytest test function with assertions.
4
Reusable SparkSession with Pytest Fixtures
Use pytest fixtures to create a reusable local SparkSession for tests.
5
Unit Testing PySpark DataFrame Transformations
Write unit tests for individual PySpark DataFrame transformation functions.
6
Testing Transformations with pytest.mark.parametrize
Use pytest's parametrize feature to test transformations with multiple input scenarios.
7
Running Pytest and Interpreting Results
Run pytest from the command line and interpret test results and failure reports.
Previous module
Next module
Data Quality Testing and Code Standards