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 3~~00 USD 259 ~~USD~~~~ 89 USD Or ~~13000 INR~~ ~~12900 INR~~ 6900 Rupees

Enroll For Free Demo

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:

👩 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
Lifetime 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)

Introduction to ETL

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

ETL Architecture

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

Data Processing Types

Full load
Incremental load
CDC basics
Batch processing
Streaming overview

ETL Lifecycle

Requirement analysis
Mapping document understanding
Extraction
Transformation
Loading
Validation

ETL Testing Fundamentals

Source validation
Transformation validation
Target validation
Reporting validation

Data Quality Concepts

Completeness
Accuracy
Consistency
Duplicate handling
Null handling

Common ETL Issues

Duplicate records
Missing records
Schema mismatch
Incorrect transformations
Data truncation

MODULE 2 — SQL for ETL Validation (6 Hours)

SQL Fundamentals

SELECT
WHERE
GROUP BY
HAVING
ORDER BY

Joins for ETL Validation

INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL JOIN

Validation Queries

Row count validation
Duplicate validation
Null validation
Aggregate validation
Lookup validation

Source-to-Target Reconciliation

Record comparison
Minus/Except validation
Hash comparison basics

Transformation Validation

Derived column validation
Default value validation
Data type validation

Incremental & CDC Validation

Timestamp validation
Insert/update/delete validation

SCD Validation

SCD Type 1
SCD Type 2
Historical validation basics

Advanced SQL Concepts

CTEs
Window functions
Ranking functions

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

Python Fundamentals

Variables
Data types
Operators

Collections

Lists
Tuples
Dictionaries
Sets

Control Flow

Conditions
Loops
Functions

File Handling

CSV handling
JSON handling
Config file handling

Exception Handling

try-except
Custom exception basics

Python Utilities

Database connectivity basics
Dynamic SQL execution
Logging basics

Introduction to Pytest

Assertions
Fixtures
Parameterization basics
MODULE 4 — PySpark for ETL Validation (9 Hours)ObjectiveLearn 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
1. PySpark Environment Setup
Topics
- Installing PySpark
- SparkSession creation
- Running PySpark scripts
- Notebook execution basics
1. PySpark DataFrames
Topics
- Creating DataFrames
- Reading CSV files
- Reading JSON files
- Reading Parquet files
- Schema inference
- Manual schema definition
1. DataFrame Transformations
Topics
- select
- filter
- where
- withColumn
- drop
- rename
- cast
- orderBy
1. Joins & Aggregations
Topics
- Inner join
- Left join
- Right join
- Full join
- groupBy
- aggregations
- distinct
- dropDuplicates
1. 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
1. Incremental Validation Using PySpark
Topics
- Delta comparison
- Partition validation
- Timestamp validation
- Snapshot comparison basics
1. PySpark SQL
Topics
- Creating temporary views
- Running SQL queries in Spark
- SQL-based reconciliation
1. PySpark Performance Basics
Topics
- Partitioning basics
- Caching basics
- Broadcast joins overview
- Shuffle overview
1. Error Handling & Logging in PySpark
Topics
- Handling bad records
- Exception handling
- Validation logging
- Audit logging basics
1. PySpark Validation Framework Concepts
Topics
- Reusable validation functions
- Dynamic validation execution
- Config-driven validation
- Validation result generation
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
1. Project Folder Structure
Structure
- configs
- input
- output
- utilities
- testcases
- reports
- logs
1. Reusable Validation Development
Build Generic Validators
- Row count validator
- Duplicate validator
- Null validator
- Schema validator
- Reconciliation validator
1. Config-Driven Validation
Topics
- Reading validation configs
- Dynamic execution
- Parameterized validations
1. 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
1. Reporting Framework
Topics
- HTML reports
- Validation summary reports
- Failed record capture
- Error logging integration
1. End-to-End Framework Execution
Execution Flow
- Config loading
- Data ingestion
- Validation execution
- Pytest execution
- Report generation
1. Framework Enhancements
Topics
- Reusable utilities
- Dynamic environments
- Logging improvements
- Validation extensibility
Live Sessions Price:

For LIVE sessions – Offer price after discount is 3~~00 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

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ETL Automation Testing with PySpark & Pytest

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