ETL Automation Testing with PySpark & Pytest

ETL Automation Testing with PySpark & Pytest – Live Training

(Learn End-to-End ETL Validation, Data Quality Testing, PySpark Automation, SQL Reconciliation, and Framework Development with Real-Time Projects)

 

This comprehensive ETL Testing Automation course is designed to help learners master modern ETL and Big Data testing concepts using SQL, Python, PySpark, and Pytest. The training covers end-to-end ETL workflows including source-to-target validation, data reconciliation, transformation testing, incremental and CDC validation, data quality checks, and automation framework development. Learners will gain hands-on experience with PySpark DataFrames, SQL validation techniques, reusable validation functions, and dynamic ETL testing strategies used in real-time enterprise projects.

The course is highly practical and industry-focused, with complete coverage of ETL automation framework development using PySpark and Pytest. Participants will work on real-time project scenarios involving validation execution, reporting, logging, and automated test execution. This training is ideal for Manual Testers, ETL Testers, Automation Engineers, Data QA Professionals, and freshers who want to build a career in ETL Testing, Big Data Testing, or Data Engineering QA Automation.

 

About the Instructor:

Haran is a passionate and highly experienced Data Professional with over 13 years of expertise in ETL Testing, ETL Automation Testing, Cloud Data Integration, and Azure Data Engineering. Throughout his career, he has successfully designed, implemented, automated, and validated complex enterprise data pipelines and cloud migration solutions using leading technologies such as Azure Data Factory (ADF), Azure Synapse Analytics, Azure Data Lake, SQL, PySpark, Power BI, and modern ETL Automation Frameworks. He possesses strong hands-on experience in SQL-driven ETL Transformations, Automated Data Validation Frameworks, Real-Time Data Quality Monitoring, Reconciliation Testing, and End-to-End ETL Automation using PySpark & Pytest. Haran is highly passionate about teaching and strongly believes in practical, real-time, and project-oriented learning methodologies. His training sessions are highly interactive, industry-focused, and designed around real-world project scenarios, helping learners gain project-ready skills and confidence to work in modern Cloud and Big Data environments. Known for his clear explanation style and learner-friendly approach, Haran has successfully trained and mentored 300+ students and working professionals in ETL Testing, Data Engineering, Azure Analytics, and ETL Automation Technologies. His dedication towards mentoring and knowledge sharing has helped many professionals successfully transition into high-demand Cloud Data Engineering and ETL Automation roles.

 

Live Sessions  Price:

For LIVE sessions – Offer price after discount is 300 USD 259 USD 89 USD Or 13000 INR 12900 INR 6900 Rupees

Enroll For Free Demo

OR

WhatsAp

 

Free Demo On:

15th June @ 9:00 PM – 10:00 PM (IST) (Indian Timings)/

15th June @ 11:30 AM –12:30 PM (EST) (U.S Timings)/

15th June @ 4:30 PM – 5:30 PM (BST) (U.K Timings)

Class Schedule:

For Participants in India: Monday to Friday @ 9:00 PM – 10:00 PM (IST)

For Participants in the US: Monday to Friday @ 11:30 AM –12:30 PM (EST)

For Participants in the UK: Monday to Friday @ 4:30 PM – 5:30 PM (BST)

 

What students have to say about Haran:

👩varalakshmi somu The instructor, Haran, is very knowledgeable in the ETL Testing course. We had highly interactive classes, which helped me gain knowledge and skills in SQL and Data warehouse concepts. He was always patient and willing to answer any questions we had, no matter how simple or advanced. The hands-on examples and real-world use cases were especially helpful for solidifying what I learned. 👨Akshay Patil The way is Teaching is good. Haran is repeating the concept with different set of examples until you are clear with that. 👩Archana C Haran explained everything in an easy and understandable way. He is very knowledgeable and always tries to find answers when asked about any topic. His teaching style is patient, supportive, and helpful for understanding ETL concepts clearly. 👨venkat pradeep Haran explained the concepts of SQL, ETL Validations, Automation frame works and Azure were very clear & informative. Great Learning experience. Thank you so much. 👩 Fatima:  The instructor explained every concept in a clear and structured way. The real-world examples in SQL and Python made it easier to connect theory with practice. I felt confident applying these skills to my projects. 👨 Arjun Mehta: Amazing teaching style with lots of practical exercises. The sessions were interactive and engaging. 👨 James Robinson: Ravi Prasad sir is very patient and thorough in his teaching. His step-by-step approach to ETL testing and data warehousing really helped me build strong fundamentals. I would definitely recommend him to anyone starting their data journey. 👩 Sophia Rodriguez:  Very knowledgeable and approachable instructor. He makes even complex SQL queries simple to understand. I truly feel this course has boosted my career prospects. 👨 Vamshi Krishna: I really enjoyed the course. The instructor gave real-time examples that made the concepts easy to grasp.

 

Salient Features:

• 30 Hours of Live Training along with recorded videos
• One Year access to the recorded videos
• Course Completion Certificate

 

Who can enroll for this course?

• Manual Testers looking to move into ETL Testing & Automation
• ETL Testers who want to learn PySpark-based automation
• Automation Test Engineers interested in Data & Big Data Testing
• Data Engineers who want to strengthen their data validation and testing skills
• QA Professionals working on Data Warehouse or Cloud Data projects
• Freshers and Graduates interested in building a career in ETL Testing or Data Engineering
• Professionals planning to transition into Big Data Testing, Azure Data Engineering, or ETL Automation roles
• Anyone interested in learning real-time ETL Automation Framework Development using PySpark & Pytest.

 

What will I learn by the end of this course?

• Understand complete ETL & Data Warehouse concepts and real-time ETL workflows.
• Perform ETL Testing and Data Validation using advanced SQL techniques.
• Validate source-to-target mappings, transformations, data quality, duplicates, nulls, and reconciliation scenarios.
• Work with Incremental Loads, CDC Validation, and SCD Testing concepts.
• Build automation scripts using Python for ETL validation processes.
• Master PySpark DataFrame operations for large-scale data validation and testing.
• Perform ETL Automation Testing using PySpark & Pytest in real-time scenarios.
• Develop Reusable and Config-Driven Validation Frameworks for enterprise projects.
• Implement Dynamic Test Execution, Logging, Reporting, and HTML Report Generation.
• Execute complete End-to-End ETL Automation Frameworks using industry best practices.
• Gain practical exposure through real-time project implementation and hands-on exercises.
• Become job-ready for roles such as ETL Tester, ETL Automation Engineer, Big Data Tester, and Data QA Engineer.

 

Course syllabus:

MODULE 1 — ETL Fundamentals & Data Flow (5 Hours)

1. Introduction to ETL

• What is ETL
• ETL vs ELT
• Purpose of ETL pipelines
• End-to-end data flow overview

2. ETL Architecture

• Source systems
• Landing layer
• Staging layer
• Transformation layer
• Warehouse layer
• Reporting layer

3. Data Processing Types

• Full load
• Incremental load
• CDC basics
• Batch processing
• Streaming overview

4. ETL Lifecycle

• Requirement analysis
• Mapping document understanding
• Extraction
• Transformation
• Loading
• Validation

5. ETL Testing Fundamentals

• Source validation
• Transformation validation
• Target validation
• Reporting validation

6. Data Quality Concepts

• Completeness
• Accuracy
• Consistency
• Duplicate handling
• Null handling

7. Common ETL Issues

• Duplicate records
• Missing records
• Schema mismatch
• Incorrect transformations
• Data truncation

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MODULE 2 — SQL for ETL Validation (6 Hours)

1. SQL Fundamentals

• SELECT
• WHERE
• GROUP BY
• HAVING
• ORDER BY

2. Joins for ETL Validation

• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL JOIN

3. Validation Queries

• Row count validation
• Duplicate validation
• Null validation
• Aggregate validation
• Lookup validation

4. Source-to-Target Reconciliation

• Record comparison
• Minus/Except validation
• Hash comparison basics

5. Transformation Validation

• Derived column validation
• Default value validation
• Data type validation

6. Incremental & CDC Validation

• Timestamp validation
• Insert/update/delete validation

7. SCD Validation

• SCD Type 1
• SCD Type 2
• Historical validation basics

8. Advanced SQL Concepts

• CTEs
• Window functions
• Ranking functions

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MODULE 3 — Python Basics for ETL Automation ( 5 Hours)

1. Python Fundamentals

• Variables
• Data types
• Operators

2. Collections

• Lists
• Tuples
• Dictionaries
• Sets

3. Control Flow

• Conditions
• Loops
• Functions

4. File Handling

• CSV handling
• JSON handling
• Config file handling

5. Exception Handling

• try-except
• Custom exception basics

6. Python Utilities

• Database connectivity basics
• Dynamic SQL execution
• Logging basics

7. Introduction to Pytest

• Assertions
• Fixtures
• Parameterization basics

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MODULE 4 — PySpark for ETL Validation (9 Hours)

Objective

Learn enterprise ETL validation techniques using PySpark.

1. Introduction to PySpark

Topics

• Why PySpark for ETL testing
• Spark ecosystem overview
• Spark architecture basics
• Driver and executor concepts
• Lazy evaluation
• DAG basics

2. PySpark Environment Setup

Topics

• Installing PySpark
• SparkSession creation
• Running PySpark scripts
• Notebook execution basics

3. PySpark DataFrames

Topics

• Creating DataFrames
• Reading CSV files
• Reading JSON files
• Reading Parquet files
• Schema inference
• Manual schema definition

4. DataFrame Transformations

Topics

• select
• filter
• where
• withColumn
• drop
• rename
• cast
• orderBy

5. Joins & Aggregations

Topics

• Inner join
• Left join
• Right join
• Full join
• groupBy
• aggregations
• distinct
• dropDuplicates

6. ETL Validation Using PySpark

Source-to-Target Validation

• Row count validation
• Column comparison
• Data reconciliation
• Record mismatch detection

Data Quality Validation

• Duplicate validation
• Null validation
• Schema validation
• Data type validation
• Mandatory column validation

Transformation Validation

• Derived column validation
• Aggregate validation
• Lookup validation
• Business rule validation

7. Incremental Validation Using PySpark

Topics

• Delta comparison
• Partition validation
• Timestamp validation
• Snapshot comparison basics

8. PySpark SQL

Topics

• Creating temporary views
• Running SQL queries in Spark
• SQL-based reconciliation

9. PySpark Performance Basics

Topics

• Partitioning basics
• Caching basics
• Broadcast joins overview
• Shuffle overview

10. Error Handling & Logging in PySpark

Topics

• Handling bad records
• Exception handling
• Validation logging
• Audit logging basics

11. PySpark Validation Framework Concepts

Topics

• Reusable validation functions
• Dynamic validation execution
• Config-driven validation
• Validation result generation

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MODULE 5 — ETL Automation Project Using PySpark & Pytest (5 Hours)

Objective

Build complete ETL testing automation framework.

1. Project Architecture

Flow

Source Files →
PySpark Processing →
Validation Layer →
Pytest Execution →
HTML Reporting

2. Project Folder Structure

Structure

• configs
• input
• output
• utilities
• testcases
• reports
• logs

3. Reusable Validation Development

Build Generic Validators

• Row count validator
• Duplicate validator
• Null validator
• Schema validator
• Reconciliation validator

4. Config-Driven Validation

Topics

• Reading validation configs
• Dynamic execution
• Parameterized validations

5. Pytest Integration

Pytest Fundamentals

• Assertions
• Fixtures
• Parameterization
• conftest.py basics

ETL Automation Using Pytest

• Executing PySpark validations
• Dynamic test execution
• Batch validation execution
• Validation result assertions

6. Reporting Framework

Topics

• HTML reports
• Validation summary reports
• Failed record capture
• Error logging integration

7. End-to-End Framework Execution

Execution Flow

• Config loading
• Data ingestion
• Validation execution
• Pytest execution
• Report generation

8. Framework Enhancements

Topics

• Reusable utilities
• Dynamic environments
• Logging improvements
• Validation extensibility

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Bonus Topics:

1. Fundamentals of AI-Driven ETL Testing & Test Generation

Topics

• Introduction to AI in Data Quality
• Automated Test Case Generation
• SQL Query Generation for Testers

2. Data Quality & Schema Validation with Google Gemini & Claude

Topis

• Automating Data Profiling
• Constraint & Schema Testing
• Mock Data Generation

3. Advanced Transformations & Hands-on Testing (Python & Power Query)

Topics

• Validating Python/Pandas ETL Pipelines
• Power Query Logic Verification
• Simulating Real-Time Failures

4. Workflow Automation & Low-Code AI Testing (n8n Integration)

Topics

• Introduction to n8n for Testing Pipelines
• Functional Automation Tools
• Automating Regression Test Suites

 

 

Live Sessions  Price:

For LIVE sessions – Offer price after discount is 300 USD 259 USD 89 USD Or 13000 INR 12900 INR 6900 Rupees

Enroll For Free Demo

OR

For any other details, Call me or Whatsapp me on +91-9133190573

Sample Course Completion Certificate:

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Important Note:

To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.

To reiterate, moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.

We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.

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