ETL Automation Testing with Pytest & PySpark

ETL Automation Testing with Pytest & PySpark – Live Training

(Learn ETL Validation, Data Quality Testing, SQL Reconciliation, Azure Data Testing, PySpark Automation & End-to-End Test Framework Development)

 

This comprehensive ETL Testing Automation course is designed to help learners master modern ETL, Data Testing, and Big Data validation concepts using SQL, SSIS, Azure Data Factory, Python, PySpark, Pytest, Databricks, and Power BI. The training covers end-to-end ETL testing including source-to-target validation, data reconciliation, transformation testing, row count validation, schema and data type validation, duplicate and NULL validation, incremental load testing, business-rule validation, data-quality checks, and Power BI data validation. Learners will gain practical experience with Azure Data Lake, ADF pipelines, Databricks, PySpark DataFrames, Delta tables, SQL validation queries, and reusable Python validation functions.

The course is highly practical and industry-focused, with dedicated coverage of ETL automation framework development using Python, PySpark, and Pytest. Participants will learn to create reusable validation functions, automate ETL test cases, execute complete test suites, implement logging and reporting, capture failed records, and analyze validation results. The training also includes an end-to-end ETL testing project covering SSIS, Azure Data Factory, Azure Data Lake, Databricks, PySpark, SQL, Python, Pytest, and Power BI. This course is ideal for Manual Testers, ETL Testers, Automation Engineers, Data QA Professionals, Big Data Testers, Data Engineers, and freshers looking to build careers in ETL Testing, Data Testing, Big Data Testing, or Data 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

WhatsApp

 

Free Demo On:

21st September @ 9:00 PM – 10:00 PM (IST) (Indian Timings)/

21st September @ 11:30 AM –12:30 PM (EST) (U.S Timings)/

21st September @ 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:

• 40 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.

 

Technologies Covered :

Technology	Main Purpose
SQL	Data validation & reconciliation
SSIS	Traditional ETL testing
Azure Data Lake Storage Gen2	File & data validation
Azure Data Factory	Cloud ETL pipeline testing
Databricks	Big-data processing validation
PySpark	Large-volume data validation
Python	ETL test automation
Pytest	Automated test execution
Power BI	Report & data validation

 

What will I learn by the end of this course?

• Understand ETL processes and testing concepts.
• Validate SSIS ETL processes.
• Test Azure Data Factory pipelines.
• Validate files and data in Azure Data Lake Storage.
• Understand Databricks and PySpark processing.
• Perform source-to-target validation using SQL.
• Perform large-volume data validation using PySpark.
• Use Python to create reusable ETL validation utilities.
• Automate ETL test cases using Pytest.
• Validate Power BI data and reports.
• Build a reusable ETL automation testing framework.
• Execute an end-to-end ETL automation testing project.

Course syllabus:

MODULE 1 — ETL & SQL for Data Testers  (5 Hours)

Objective

Understand ETL fundamentals and learn SQL-based validation techniques required for ETL testing.

1. Introduction to ETL

• What is ETL?
• Why ETL is required
• ETL vs ELT
• ETL pipeline concepts
• Source, transformation and target concepts
• Batch processing
• Incremental processing
• ETL Tester responsibilities

2. ETL Testing Fundamentals

• What is ETL testing?
• Source validation
• Transformation validation
• Target validation
• Data-quality validation
• Business-rule validation
• Functional testing
• Regression testing
• Smoke testing

3. SQL Fundamentals for Data Testers

• SELECT
• WHERE
• DISTINCT
• ORDER BY
• GROUP BY
• HAVING
• CASE
• Aggregate functions
• NULL handling

4. SQL Joins for ETL Testing

• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL JOIN
• Self Join basics

5. ETL Validation Queries

• Row count validation
• Duplicate validation
• NULL validation
• Primary-key validation
• Aggregate validation
• Source-to-target validation
• Missing-record validation
• Extra-record validation

6. Advanced SQL for ETL Testing

• CTE
• Subqueries
• Window functions
• ROW_NUMBER
• RANK
• LEAD
• LAG

Hands-on

Create SQL queries for:

• Count validation
• Duplicate validation
• NULL validation
• Source-to-target reconciliation
• Transformation validation

---

MODULE 2 — SSIS ETL Testing (5 Hours)

Objective

Understand SSIS from a Data Tester perspective and perform complete SSIS ETL validation.

1. Introduction to SSIS

• What is SSIS?
• Why SSIS is used
• SSIS packages
• Projects
• Control Flow
• Data Flow

2. Important SSIS Components

• Execute SQL Task
• Data Flow Task
• Flat File Source
• OLE DB Source
• OLE DB Destination
• Lookup
• Derived Column
• Conditional Split
• Data Conversion
• Aggregate

3. SSIS Variables & Parameters

• Variables
• Package parameters
• Project parameters
• Expressions
• Dynamic values

4. SSIS ETL Testing

Validate:

• Source records
• Target records
• Record counts
• Column mapping
• Data types
• NULL values
• Duplicate records
• Lookup results
• Derived columns
• Business rules

5. SSIS Incremental Load Testing

• Full load
• Incremental load
• Timestamp-based incremental load
• New records
• Updated records
• Duplicate processing
• Reprocessing scenarios

6. SSIS Error & Reject Record Validation

• Error output
• Rejected records
• Invalid records
• Error tables
• Data-quality errors
• Validation of rejected records

Hands-on

Create an SSIS package and perform:

• Count validation
• Data validation
• Transformation validation
• Duplicate validation
• NULL validation
• Incremental-load validation

---

MODULE 3 — Azure Fundamentals for Data Testers ( 4 Hours)

Objective

Understand the Azure services required for ETL testing without going into unnecessary Azure administration topics.

1. Introduction to Microsoft Azure

• What is Azure?
• Azure Portal
• Resource Groups
• Azure resources
• Azure Storage basics

2. Azure Data Services for ETL Testing

• Azure Blob Storage
• Azure Data Lake Storage Gen2
• Azure Data Factory
• Azure Databricks

3. Azure Data Lake Storage Gen2

• Storage accounts
• Containers
• Folders
• Files
• CSV files
• JSON files
• Parquet files

4. Data Tester Activities in Azure

• File validation
• File availability validation
• File name validation
• File count validation
• File format validation
• File schema validation
• Data validation
• Record-count validation

5. Azure Data Tester Concepts

• Understanding Azure-based ETL processes
• Source data validation
• Processed data validation
• Target validation
• Incremental data validation
• Data-quality validation

Hands-on

Perform basic file and data validations using Azure storage.

---

MODULE 4 — Azure Data Factory & ETL Pipeline Testing  (6 Hours)

Objective

Learn how to test Azure Data Factory pipelines from a Data Tester perspective.

1. Introduction to Azure Data Factory

• What is ADF?
• Why ADF is used
• ADF workspace
• Pipelines
• Activities
• Datasets
• Linked Services
• Integration Runtime

2. ADF Activities

• Copy Data
• Lookup
• Get Metadata
• Stored Procedure
• Script
• Execute Pipeline
• ForEach
• If Condition
• Set Variable

3. ADF Parameters & Variables

• Pipeline parameters
• Dataset parameters
• Variables
• Dynamic expressions
• Dynamic file names
• Dynamic paths

4. ADF Pipeline Testing

Validate:

• Pipeline execution
• Activity execution
• Source data
• Target data
• File movement
• Record counts
• Data types
• Schema
• Data transformation

5. ADF Copy Activity Testing

• Source validation
• Destination validation
• File validation
• Column mapping validation
• Data type validation
• Record count validation
• Data reconciliation

6. ADF Incremental Load Testing

• Full load testing
• Incremental load testing
• Timestamp validation
• Watermark concept
• New record validation
• Updated record validation
• Duplicate processing validation

7. ADF Parameter Testing

• Valid parameters
• Different parameter values
• Dynamic file paths
• Dynamic dates
• Multiple datasets
• Parameter-driven execution

Hands-on

Create and test an ADF pipeline involving:

• Source file
• Copy Activity
• Data Lake
• Parameter-driven execution
• Incremental processing

---

MODULE 5 — Python for ETL Test Automation  (5 Hours)

Objective

Learn Python specifically for automating ETL testing.

1. Python Fundamentals

• Variables
• Data types
• Strings
• Numbers
• Boolean
• Lists
• Tuples
• Sets
• Dictionaries

2. Python Operators

• Arithmetic operators
• Comparison operators
• Logical operators
• Assignment operators
• Membership operators

3. Control Flow

• if
• elif
• else
• for loop
• while loop
• break
• continue
• pass

4. Python Functions

• Creating functions
• Parameters
• Arguments
• Return values
• Reusable functions

5. File Handling

• Reading CSV
• Writing CSV
• Reading JSON
• Writing JSON
• Configuration files

6. Python Exception Handling

• try
• except
• else
• finally
• Custom exceptions

7. Python + SQL

• Database connectivity basics
• Execute SQL
• Fetch data
• Row-count validation
• Dynamic SQL
• Source-to-target comparison

8. Python ETL Validation Functions

Create reusable functions:

• validate_count()
• validate_nulls()
• validate_duplicates()
• validate_schema()
• validate_data()
• validate_aggregate()

9. Python Logging

• Logging basics
• INFO
• WARNING
• ERROR
• Validation logs
• Error logs

Hands-on

Develop a Python ETL validation utility that produces:

• Validation Name
• Expected Result
• Actual Result
• Status
• Error Message

 

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MODULE 6 — Azure Databricks & PySpark ETL Testing  (6 Hours)

Objective

Learn Databricks and PySpark specifically for large-volume ETL validation.

1. Introduction to Azure Databricks

• What is Databricks?
• Why Databricks is used
• Databricks workspace
• Notebooks
• Clusters
• Jobs
• Tables
• Files

2. Apache Spark Fundamentals

• What is Spark?
• Why Spark is used
• Driver
• Executors
• Tasks
• Jobs
• Stages
• DAG
• Lazy evaluation

3. PySpark DataFrames

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

4. PySpark Transformations

• select
• filter
• where
• withColumn
• drop
• alias
• cast
• orderBy
• distinct
• dropDuplicates

5. PySpark Joins

• Inner join
• Left join
• Right join
• Full join
• Lookup validation
• Join-result validation

6. PySpark Aggregations

• groupBy
• count
• sum
• avg
• min
• max
• countDistinct

7. ETL Validation Using PySpark

• Row count validation
• Column validation
• Schema validation
• Data type validation
• NULL validation
• Duplicate validation
• Data reconciliation
• Aggregate validation
• Derived-column validation
• Business-rule validation

8. Delta Table Validation

• Delta tables
• Reading Delta tables
• Writing Delta tables
• Schema validation
• Data validation
• Basic time-travel concept

9. Incremental Validation

• New records
• Updated records
• Deleted records
• Timestamp comparison
• Partition validation
• Snapshot comparison

10. PySpark Performance Basics for Testers

• Partitioning basics
• Repartition
• Coalesce
• Cache
• Shuffle overview
• Broadcast join overview

Hands-on

Perform PySpark validations against large datasets:

• Count
• Schema
• NULL
• Duplicate
• Data Comparison
• Transformation
• Aggregate

 

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MODULE 7 — Power BI Data & Report Testing  (2 Hours)

Objective

Learn how Data Testers can validate Power BI reports and the underlying data.

1. Introduction to Power BI Testing

• What is Power BI?
• Power BI Desktop
• Power BI Service
• Dataset
• Report
• Dashboard
• Visuals

2. Power BI Data Validation

• Source-to-Power BI validation
• Record-count validation
• Column validation
• Data-type validation
• NULL validation
• Duplicate validation
• Aggregate validation

3. Power BI Report Testing

Validate:

• Card values
• Tables
• Charts
• Filters
• Slicers
• Date filters
• Drill-down
• Drill-through

4. Power BI Transformation Testing

• Power Query basics
• Data filtering
• Column transformations
• Derived columns
• Data type changes

5. DAX Validation Basics

• Measures
• Calculated columns
• SUM
• COUNT
• DISTINCTCOUNT
• CALCULATE basics

6. Power BI Reconciliation

Validate:

• Database values
• ETL output
• Power BI values
• Aggregates
• Business calculations

Hands-on

Validate a Power BI report against the underlying data using SQL.

---

MODULE 8— Pytest ETL Automation & End-to-End Project  (7 Hours)

Objective

Build and execute a reusable ETL automation testing framework using Python, PySpark and Pytest.

Part A — Pytest Fundamentals

1. Introduction to Pytest

• What is Pytest?
• Why Pytest for ETL testing?
• Test files
• Test functions
• Assertions

2. ETL Test Cases

Create automated tests for:

• Count validation
• NULL validation
• Duplicate validation
• Schema validation
• Data validation
• Aggregate validation
• Business-rule validation

3. Pytest Fixtures

• What is a fixture?
• Setup
• Teardown
• Reusable test setup
• Database connection fixture
• Spark session fixture

4. Parameterization

• Parameterized test cases
• Multiple datasets
• Multiple validation rules
• Reusable test execution

5. Pytest + SQL

• Execute SQL validations
• Capture query results
• Compare expected and actual results
• Assertions
• Failure messages

6. Pytest + PySpark

• Execute PySpark validations
• Compare DataFrames
• Validate records
• Validate schemas
• Assertions

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Part B — Reusable ETL Validation Framework

7. Reusable Validation Functions

Develop reusable validators:

• Count Validator
• Duplicate Validator
• NULL Validator
• Schema Validator
• Reconciliation Validator
• Transformation Validator
• Aggregate Validator
• Business Rule Validator

8. Test Execution

• Execute individual tests
• Execute multiple tests
• Execute complete test suite
• Run tests from command line
• Test execution options
• Test result analysis

9. Logging

• Execution logs
• Validation logs
• Error logs
• Failed-test details
• Execution timestamps

10. Test Reporting

• Test execution report
• PASS/FAIL status
• Failed validation details
• Failed-record capture
• HTML reporting
• Test summary

---

Part C — End-to-End ETL Automation Testing Project

Project Activities

Students will perform complete ETL testing involving:

• Source data validation
• SSIS ETL validation
• Azure Data Factory pipeline validation
• Azure Data Lake file validation
• Databricks data validation
• PySpark transformation validation
• SQL source-to-target reconciliation
• Python-based validation
• Pytest automation
• Power BI data validation
• Test reporting

Project Test Cases

TC01 — Row Count Validation

TC02 — Duplicate Validation

TC03 — NULL Validation

TC04 — Schema Validation

TC05 — Data Reconciliation

TC06 — Transformation Validation

TC07 — Aggregate Validation

TC08 — Business Rule Validation

TC09 — Incremental Load Validation

TC10 — Power BI Data Validation

Final Execution

Students will execute the complete set of automated ETL test cases and analyze:

• Passed tests
• Failed tests
• Validation results
• Error messages
• Failed records
• Execution logs
• HTML test reports

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BONUS CONTENT — AI for ETL & Data Testing  (2 Hours)

Objective

Introduce practical ways Data Testers can use AI tools to improve ETL testing, SQL validation, Python automation, and test-case preparation.

1. Introduction to AI for Data Testing

• What is Generative AI?
• How AI can assist Data Testers
• Benefits of AI in ETL Testing
• Limitations of AI-generated test cases and code
• Importance of tester review and validation

2. AI-Assisted ETL Test Case Generation

Use AI to generate test cases for:

• Source-to-target validation
• Row count validation
• Duplicate validation
• NULL validation
• Schema validation
• Data-type validation
• Transformation validation
• Business-rule validation
• Incremental-load testing

3. AI-Assisted SQL Generation

Use AI to help create:

• SQL validation queries
• Count comparison queries
• Duplicate detection queries
• NULL validation queries
• Source-to-target reconciliation queries
• Aggregate validation queries
• Missing-record queries
• Extra-record queries
• Transformation validation queries

4. AI-Assisted Python Automation

Use AI to assist with:

• Python validation scripts
• Reusable validation functions
• CSV/JSON processing
• Database connectivity code
• Exception handling
• Logging
• Pytest test cases
• Debugging Python errors

5. AI-Assisted PySpark Testing

Use AI to help generate:

• PySpark DataFrame validation
• Schema validation
• Duplicate validation
• NULL validation
• Data comparison
• Transformation validation
• Aggregation validation
• PySpark test cases

6. AI-Assisted ETL Defect Analysis

Use AI to analyze:

• SQL errors
• Python errors
• PySpark errors
• ETL validation failures
• Data mismatches
• Schema mismatches
• Duplicate records
• Transformation issues

7. AI-Assisted Test Documentation

Use AI to generate:

• Test scenarios
• Test cases
• Test data ideas
• Expected results
• Defect descriptions
• Test execution summaries
• Test documentation

8. Practical Hands-on Exercises

Students will use an AI tool to:

• Generate SQL validation queries
• Generate Python validation code
• Generate Pytest test cases
• Analyze a failed ETL validation
• Improve an existing validation script
• Generate additional negative test scenarios

Important Testing Principle

AI-generated output should never be accepted blindly.

The tester must:

• Review the generated SQL/code
• Verify the business logic
• Execute the query/script
• Compare results with expected results
• Correct AI-generated mistakes
• Protect sensitive project information

 

Frequently Asked Questions (FAQs)

1. What is ETL Testing?
ETL Testing is the process of validating data as it moves from source systems through transformation processes to target systems. It includes data validation, reconciliation, transformation testing, data quality checks, and business-rule validation.

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2. What will I learn in this ETL Testing course?
You will learn SQL, ETL testing fundamentals, SSIS, Azure Data Factory, Azure Data Lake, Databricks, Python, PySpark, Pytest, Power BI testing, and end-to-end ETL automation.

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3. Is SQL covered in the ETL Testing training?
Yes. The course covers SQL fundamentals, joins, aggregate functions, CTEs, subqueries, window functions, and practical validation queries for row counts, duplicates, NULLs, reconciliation, and transformations.

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4. Will I learn SSIS ETL Testing?
Yes. You will learn SSIS components, data flow, control flow, variables and parameters, incremental-load testing, transformation validation, error handling, and rejected-record validation.

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5. Does the course cover Azure Data Factory (ADF) Testing?
Yes. You will learn how to test ADF pipelines, activities, datasets, linked services, parameters, dynamic paths, copy activities, incremental loads, and pipeline execution.

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6. Will I learn Azure Data Lake validation?
Yes. The training covers Azure Data Lake Storage Gen2 and practical validation of files, schemas, formats, file availability, record counts, and data.

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7. Is Python included for ETL Test Automation?
Yes. Python is taught specifically for ETL testing, including file handling, exception handling, SQL connectivity, reusable validation functions, and logging.

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8. Will I learn PySpark for ETL Testing?
Yes. The course covers PySpark DataFrames, transformations, joins, aggregations, schema validation, data reconciliation, business-rule validation, Delta tables, and incremental validation.

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9. Is Databricks covered in the training?
Yes. You will learn Azure Databricks fundamentals, notebooks, clusters, jobs, tables, Spark concepts, and Databricks-based ETL data validation.

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10. Will I learn Pytest for ETL automation?
Yes. You will learn Pytest fundamentals, fixtures, parameterization, SQL integration, PySpark integration, assertions, reusable validators, test execution, logging, and reporting

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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