Part 10- Data Testing Automation Framework | Run test cases from excel using pytest (2)
Description-
🚀 What You’ll Learn in This Project
In this video, you’ll learn how to build a complete data automation framework using Python, Pandas, Supabase (Postgresql) and Pytest — from scratch!
We’ll start by downloading a CSV dataset from Kaggle, upload it into Supabase as a table, and then automate various data validation test cases using Pytest. You’ll see how to connect Supabase to Python, perform count and transformation checks, and finally generate a professional HTML test report.
By the end of this project, you’ll have a fully working data testing automation framework that’s scalable, modular, and easy to extend for real-world projects.
Topics-
Read test cases from an excel file as parameters through Pytest
Run each test cases one by one
Validate source (csv) results vs target (Postgres)
Run groupby logic on csv file to calculate aggregate values
💡 Tech Stack: Python | Pytest | Supabase (Postgres) | Pandas
📊 Source: CSV dataset from Kaggle
Link to Download Kaggle Data Set- https://www.kaggle.com/datasets/vivek...
Full Code GitHub Link- https://github.com/Jyoti13394/datates...
Link to Download PyCharm- https://www.jetbrains.com/pycharm/dow...
Link to create a free account in Supabase- • Part 3- Data Testing Automation Framework ...
Link to video to fetch Postgres Connection Details- • Part 6- Data Testing Automation Framework ...
#DataTesting #Pytest #Postgres #PythonAutomation #ETLTesting #DataQA #AutomationFramework #DataEngineering #Pandas #SDET