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ETL Automation Testing with PySpark & Pytest Day 3

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 …

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  • Price $ 6.900,00 per participant
  • Start Time 9:00 pm June 18, 2026
  • Finish Time 10:00 pm June 18, 2026
  • Capacity Limited to 100 people

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.

Sample Videos: ETL Automation Testing with PySpark & Pytest Live Training Demo Video:

ETL Automation Testing with PySpark & Pytest Live Training Day1 Video:

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 Day3 On:

18th June @ 9:00 PM – 10:00 PM (IST) (Indian Timings)/

18th June @ 11:30 AM –12:30 PM (EST) (U.S Timings)/

18th 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
  • 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
  1. ETL Architecture
  • Source systems
  • Landing layer
  • Staging layer
  • Transformation layer
  • Warehouse layer
  • Reporting layer
  1. Data Processing Types
  • Full load
  • Incremental load
  • CDC basics
  • Batch processing
  • Streaming overview
  1. ETL Lifecycle
  • Requirement analysis
  • Mapping document understanding
  • Extraction
  • Transformation
  • Loading
  • Validation
  1. ETL Testing Fundamentals
  • Source validation
  • Transformation validation
  • Target validation
  • Reporting validation
  1. Data Quality Concepts
  • Completeness
  • Accuracy
  • Consistency
  • Duplicate handling
  • Null handling
  1. Common ETL Issues
  • Duplicate records
  • Missing records
  • Schema mismatch
  • Incorrect transformations
  • Data truncation

MODULE 2 — SQL for ETL Validation (6 Hours)

  1. SQL Fundamentals
  • SELECT
  • WHERE
  • GROUP BY
  • HAVING
  • ORDER BY
  1. Joins for ETL Validation
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL JOIN
  1. Validation Queries
  • Row count validation
  • Duplicate validation
  • Null validation
  • Aggregate validation
  • Lookup validation
  1. Source-to-Target Reconciliation
  • Record comparison
  • Minus/Except validation
  • Hash comparison basics
  1. Transformation Validation
  • Derived column validation
  • Default value validation
  • Data type validation
  1. Incremental & CDC Validation
  • Timestamp validation
  • Insert/update/delete validation
  1. SCD Validation
  • SCD Type 1
  • SCD Type 2
  • Historical validation basics
  1. Advanced SQL Concepts
  • CTEs
  • Window functions
  • Ranking functions

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

  1. Python Fundamentals
  • Variables
  • Data types
  • Operators
  1. Collections
  • Lists
  • Tuples
  • Dictionaries
  • Sets
  1. Control Flow
  • Conditions
  • Loops
  • Functions
  1. File Handling
  • CSV handling
  • JSON handling
  • Config file handling
  1. Exception Handling
  • try-except
  • Custom exception basics
  1. Python Utilities
  • Database connectivity basics
  • Dynamic SQL execution
  • Logging basics
  1. 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 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

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