ETL Testing Automation with Cloud & AI - Live Training Demo

ETL Testing Automation with Cloud & AI – Live Training

(Learn SQL, SSIS, Azure Data Factory, Databricks, Python, PySpark, PyTest & Power BI)

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

Enroll For Free Demo

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.

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

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

SQL Fundamentals for Data Testers

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

SQL Joins for ETL Testing

INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL JOIN
Self Join basics

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

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.

Introduction to SSIS

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

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

SSIS Variables & Parameters

Variables
Package parameters
Project parameters
Expressions
Dynamic values

SSIS ETL Testing

Validate:

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

SSIS Incremental Load Testing

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

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.

Introduction to Microsoft Azure

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

Azure Data Services for ETL Testing

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

Azure Data Lake Storage Gen2

Storage accounts
Containers
Folders
Files
CSV files
JSON files
Parquet files

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

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.

Introduction to Azure Data Factory

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

ADF Activities

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

ADF Parameters & Variables

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

ADF Pipeline Testing

Validate:

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

ADF Copy Activity Testing

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

ADF Incremental Load Testing

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

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.

Python Fundamentals

Variables
Data types
Strings
Numbers
Boolean
Lists
Tuples
Sets
Dictionaries

Python Operators

Arithmetic operators
Comparison operators
Logical operators
Assignment operators
Membership operators

Control Flow

if
elif
else
for loop
while loop
break
continue
pass

Python Functions

Creating functions
Parameters
Arguments
Return values
Reusable functions

File Handling

Reading CSV
Writing CSV
Reading JSON
Writing JSON
Configuration files

Python Exception Handling

try
except
else
finally
Custom exceptions

Python + SQL

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

Python ETL Validation Functions

Create reusable functions:

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

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

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

Objective

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

Introduction to Azure Databricks

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

Apache Spark Fundamentals

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

PySpark DataFrames

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

PySpark Transformations

select
filter
where
withColumn
drop
alias
cast
orderBy
distinct
dropDuplicates

PySpark Joins

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

PySpark Aggregations

groupBy
count
sum
avg
min
max
countDistinct

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

Delta Table Validation

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

Incremental Validation

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

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

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

Objective

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

Introduction to Power BI Testing

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

Power BI Data Validation

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

Power BI Report Testing

Validate:

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

Power BI Transformation Testing

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

DAX Validation Basics

Measures
Calculated columns
SUM
COUNT
DISTINCTCOUNT
CALCULATE basics

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

Introduction to Pytest

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

ETL Test Cases

Create automated tests for:

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

Pytest Fixtures

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

Parameterization

Parameterized test cases
Multiple datasets
Multiple validation rules
Reusable test execution

Pytest + SQL

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

Pytest + PySpark

Execute PySpark validations
Compare DataFrames
Validate records
Validate schemas
Assertions

Part B — Reusable ETL Validation Framework

Reusable Validation Functions

Develop reusable validators:

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

Test Execution

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

Logging

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

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

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

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.

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

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

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

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

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

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

AI-Assisted Test Documentation

Use AI to generate:

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

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

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

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

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