🚀 Advanced ETL Automation Testing - Weekend batch

Python, PySpark, Pytest, Databricks + Agentic AI

📘 Course Overview

This program is designed to help you build a complete, industry-ready ETL Data Quality Automation Framework using Python, PySpark, Pytest, Databricks, CI/CD, and Agentic AI.

The course is fully hands-on and project-oriented, focusing on real-time data validation scenarios used in enterprise environments.

🔹 Module 1: Python for Automation

• Python fundamentals for testing

• Data structures & control flow

• Functions & exception handling

• OOP concepts

• Logging & debugging

• Modular framework structure

🔹 Module 2: Pandas & Data Processing

• Working with CSV, JSON, Parquet

• Data cleansing & transformations

• Handling missing data

• SQL/Database connectivity

• Building reusable validation logic

🔹 Module 3: PySpark for Big Data Validation

• Spark architecture & setup

• DataFrame operations

• Schema validation

• Aggregations & window functions

• Duplicate & null validation

• Performance optimization

🔹 Module 4: Pytest Automation Framework

• Pytest setup & test discovery

• Fixtures & parameterization

• Assertions & reporting

• Data-driven testing

• Source-to-target validation

• Spark & Databricks integration

🔹 Module 5: CI/CD for Data Quality

• CI/CD fundamentals

• GitHub Actions workflows

• Automated test execution

• Report publishing & notifications

🔹 Module 6: Databricks & Delta Lake

• Databricks architecture

• Cluster setup & notebooks

• Delta Lake concepts

• Workflow management

• Cloud data validation setup

🔹 Module 7: End-to-End Industry Project

• Source to Target validation

• SCD Type 1 & Type 2 testing

• Data reconciliation

• Data quality rules implementation

• CI/CD-based automation execution

🤖 Module 8: Agentic AI for ETL Automation

• AI-driven test case generation

• Automatic SQL & PySpark validation creation

• Prompt engineering for ETL testing

• Intelligent anomaly detection concepts

• AI-based failure analysis

• Self-healing automation concepts

🎯 Course Outcome

By the end of this program, you will be able to:

• Build a complete ETL automation framework

• Validate large-scale Spark pipelines

• Integrate testing with CI/CD

• Work in Databricks environments

• Apply AI to enhance ETL testing efficiency