ETL TESTING USING PYTHON

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The ETL Testing using Python Syllabus focuses on validating data extraction, transformation, and loading processes using Python-based testing techniques. It covers data validation, database testing, automation scripts, data quality checks, and handling large datasets in real-world scenarios. This training explains how Python is used to automate ETL testing and ensure data accuracy across systems. It is important because reliable data is critical for analytics, reporting, and business decisions. It is ideal for QA testers, data engineers, ETL developers, and professionals working in data warehousing and analytics projects.

Duration of Training : 40 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Support-Focused • Implementation

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Overview

The Syllabus

Curriculum Designed by Experts

Module 1 – Introduction to ETL & Data Warehousing

Topics
• ETL concepts (Extract, Transform, Load)
• Data warehouse architecture
• OLTP vs OLAP
• Data marts
• ETL testing lifecycle
Lab
• Understand ETL workflow with sample dataset
Assignment
• Identify ETL test cases for sales data pipeline

Module 2 – Basics of Software Testing

Topics
• Testing types (functional, regression, integration)
• Test case design techniques
• Defect lifecycle
• Agile testing concepts
Assignment
• Write test cases for ETL data validation

Module 3 – SQL for ETL Testing (Core Module)

Topics
• SQL queries (SELECT, JOIN, GROUP BY)
• Data comparison queries
• Aggregate validation
• Data integrity checks
• Stored procedures
Lab
• Validate source vs target data
Assignment
• Write SQL queries for data validation
Scenario
• Validate data migration from legacy system to warehouse

Module 4 – Python Fundamentals for Testing

Topics
• Python basics (variables, loops, functions)
• File handling
• Exception handling
• Working with CSV, JSON
Lab
• Build Python scripts for data validation
Assignment
• Compare two datasets using Python

Module 5 – Python for ETL Testing

Topics
• Data validation using Python
• Automating ETL test cases
• Data comparison scripts
• Logging and reporting
Libraries
• pandas
• numpy
Lab
• Automate data comparison
Assignment
• Build ETL validation framework using Python

Module 6 – ETL Tools Overview

Topics
• Integration patterns
• ETL vs API-based integration
• Platform events
• Change Data Capture (CDC)
Tools
• MuleSoft
Lab
• Build event-driven integration
Enterprise Case Study
• Real-time data sync between ERP and Salesforce

Module 7 – Data Validation Techniques

Topics
• Row count validation
• Data completeness
• Data transformation validation
• Duplicate checks
• Null validation
Lab
• Perform full ETL validation cycle

Module 8 – Automation Framework for ETL Testing

Topics
• Test automation framework design
• Data-driven testing
• Logging and reporting
• Scheduling ETL tests
Tools
• pytest
Lab
• Build ETL automation framework

Module 9 – API Testing for ETL

Topics
• REST API testing
• JSON validation
• API data verification
Tools
• Postman
Lab
• Validate API data before ETL process

Module 10 – Big Data ETL Testing

Topics
• Hadoop ecosystem
• Spark basics
• Data validation in big data
Tools
• Apache Spark
Scenario
• Validate large-scale data pipelines

Module 11 – CI/CD for ETL Testing

Topics
• Continuous testing
• Integration with CI/CD pipeline
Tools
• Jenkins
Lab
• Integrate ETL tests with CI/CD

🚀 Capstone Projects

🔹 Project 1 – ETL Data Validation Framework
Features
• Compare source vs target data
• Automated validation scripts
• Logging and reporting
Tools
• Python, Pandas, SQL

🔹 Project 2 – Data Migration Testing
Features
• Validate data migration
• Data consistency checks
• Error reporting
Scenario
• Legacy system → Data warehouse migration

🔹 Project 3 – Automated ETL Testing Pipeline
Features
• CI/CD integrated testing
• Automated ETL validation
• Scheduled test execution
Tools
• Python, Jenkins

📊 Job-Oriented Scenarios

1. Validate ETL data between source and target systems
2. Automate data validation using Python
3. Troubleshoot ETL job failures
4. Perform regression testing for ETL pipelines
5. Validate large datasets in production
6. Integrate ETL tests into CI/CD pipeline

💼 Career Roles

• ETL Tester
• Data QA Engineer
• Data Analyst (Testing side)
• Data Engineer (Entry-level)
• Automation Tester (Data domain)

✅ Final Outcome

After this training, learners will be able to:
✔ Validate ETL pipelines
✔ Write SQL for data validation
✔ Automate testing using Python
✔ Work with ETL tools and frameworks
✔ Handle real-time data testing scenarios

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Etl Testing Using Python Course Certification With Training In Kochi

Q: Module 1 – Introduction to ETL & Data Warehousing

Topics • ETL concepts (Extract, Transform, Load) • Data warehouse architecture • OLTP vs OLAP • Data marts • ETL testing lifecycle Lab • Understand ETL workflow with sample dataset Assignment • Identify ETL test cases for sales data pipeline

Q: Module 2 – Basics of Software Testing

Topics • Testing types (functional, regression, integration) • Test case design techniques • Defect lifecycle • Agile testing concepts Assignment • Write test cases for ETL data validation

Q: Module 3 – SQL for ETL Testing (Core Module)

Topics • SQL queries (SELECT, JOIN, GROUP BY) • Data comparison queries • Aggregate validation • Data integrity checks • Stored procedures Lab • Validate source vs target data Assignment • Write SQL queries for data validation Scenario • Validate data migration from legacy system to warehouse

Q: Module 4 – Python Fundamentals for Testing

Topics • Python basics (variables, loops, functions) • File handling • Exception handling • Working with CSV, JSON Lab • Build Python scripts for data validation Assignment • Compare two datasets using Python

Q: Module 5 – Python for ETL Testing

Topics • Data validation using Python • Automating ETL test cases • Data comparison scripts • Logging and reporting Libraries • pandas • numpy Lab • Automate data comparison Assignment • Build ETL validation framework using Python

Q: Module 6 – ETL Tools Overview

Topics • Integration patterns • ETL vs API-based integration • Platform events • Change Data Capture (CDC) Tools • MuleSoft Lab • Build event-driven integration Enterprise Case Study • Real-time data sync between ERP and Salesforce

Q: Module 7 – Data Validation Techniques

Topics • Row count validation • Data completeness • Data transformation validation • Duplicate checks • Null validation Lab • Perform full ETL validation cycle

Q: Module 8 – Automation Framework for ETL Testing

Topics • Test automation framework design • Data-driven testing • Logging and reporting • Scheduling ETL tests Tools • pytest Lab • Build ETL automation framework

Q: Module 9 – API Testing for ETL

Topics • REST API testing • JSON validation • API data verification Tools • Postman Lab • Validate API data before ETL process

Q: Module 10 – Big Data ETL Testing

Topics • Hadoop ecosystem • Spark basics • Data validation in big data Tools • Apache Spark Scenario • Validate large-scale data pipelines

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