ETL Testing Automation with Python, Snowflake, dbt & PySpark- Live Training Day2

ETL Testing Automation with Python, Snowflake, dbt & PySpark – Live Training

(ETL Testing, SQL, Python Automation, Snowflake, dbt, PySpark, Airflow, CDC Testing, Data Quality Engineering, Cloud Data Warehouse Validation, Real-Time Projects & Interview Prep)

Master Advanced ETL Testing Automation with industry-demand technologies including SQL, Python, Snowflake, dbt, PySpark, Airflow, and Cloud Data Warehouses. This comprehensive course covers ETL Testing, Data Warehouse Testing, Data Quality Engineering, Big Data Validation, Real-Time Data Pipeline Validation, Incremental & CDC Testing, and ETL Automation using Python. Students will gain hands-on experience in validating enterprise data pipelines, cloud data warehouse migrations, transformation testing, and modern data engineering workflows through real-time projects and practical scenarios.

The program is designed for freshers, manual testers, ETL testers, automation engineers, and data professionals looking to build expertise in modern data testing and cloud-based ETL validation. Learn Snowflake Testing, dbt Transformation Validation, PySpark Data Validation, CI/CD for Data Testing, and Data Quality Monitoring with real-world use cases from e-commerce, telecom, and insurance domains. The course also includes interview preparation, mock interviews, resume building, and project explanations to help students confidently secure ETL Testing, Data Engineering QA, and Data Validation roles in leading companies.

Live Sessions Price:

Offer price after discount is ~~200 USD 159 ~~USD~~~~ 89 USD Or ~~15000 INR 9900 INR~~ 6900 Rupees.

Enroll For Free Demo

Sample Videos:

ETL Testing Automation with Python, Snowflake, dbt & PySpark – Demo Video

ETL Testing Automation with Python, Snowflake, dbt & PySpark – Day1 Video

Free Day2 On:

22nd May @ 8:00 PM – 9:00 PM (IST) (Indian Timings)/

22nd May @ 10:30 AM –11:30 AM (EST) (U.S Timings)/

22nd May @ 3:30 PM – 4:30 PM (BST) (U.K Timings)

Class Schedule:

For Participants in India: Monday to Friday @ 8:00 PM – 9:00 PM (IST)

For Participants in the US: Monday to Friday @ 10:30 AM –11:30 AM (EST)

For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)

What student’s have to say about Trainer :

👨Rahith Kumar- The training was very detailed and completely practical-oriented. Before joining this course, I only knew SQL basics, but now I am confident in ETL Validation, Snowflake Testing, dbt transformations, and Python Automation. The interview preparation sessions and mock interviews were extremely useful.

👩Niharika Netha- Excellent course for learning modern ETL Testing and Automation. The Snowflake and dbt concepts were explained with real-time examples and hands-on practice.

👨Jhoseph- The real-time projects included in the course were excellent. We worked on e-commerce and telecom-based data validation scenarios which helped me understand how ETL Testing works in actual projects. The Snowflake and CDC Testing modules were the best part of the training.

👩Jessica-I joined this course to upgrade my career into cloud data testing. The practical exposure on Snowflake, dbt, and ETL Automation using Python gave me a lot of confidence for interviews and real-time projects.

👨 Raj Kumar- This is not just a regular ETL Testing course. It covers modern technologies like Snowflake, dbt, PySpark, Airflow, CI/CD, and Data Quality Engineering which are highly demanded in the current industry.

👩Saritha- I joined this course to upgrade my career into cloud data testing. The practical exposure on Snowflake, dbt, and ETL Automation using Python gave me a lot of confidence for interviews and real-time projects.

Salient Features:

40+ Hours of Live Training along with recorded videos
Lifetime access to the recorded videos
Course Completion Certificate

Who can enroll in this course?

Manual Testing Professionals looking to transition into ETL/Data Testing
ETL Testers who want to upgrade to modern cloud technologies
Automation Testers interested in Data Testing & ETL Automation
SQL Developers and Database Professionals
Data Analysts and BI Professionals
Freshers interested in starting a career in ETL Testing or Data Engineering QA
Software Engineers looking to learn Snowflake, dbt, and Data Warehouse Testing
Professionals interested in Cloud Data Warehouses and Big Data Validation
Anyone looking to build a career in modern Data Quality Engineering and ETL Automation

No prior ETL experience is required. The course starts from fundamentals and gradually moves to advanced real-time industry concepts.

What will I learn by the end of this course?

Understand ETL Testing concepts, Data Warehousing, and modern data pipeline architecture
Write advanced SQL queries for ETL Validation and Data Testing
Perform Source-to-Target Data Validation and Data Reconciliation
Build ETL Automation frameworks using Python
Validate Cloud Data Warehouses using Snowflake
Perform dbt Transformation Testing and Data Quality Validation
Work with Incremental Load & CDC Testing scenarios
Validate Real-Time Data Pipelines and Big Data workflows using PySpark
Test ETL workflows and scheduling using Airflow
Implement CI/CD for automated ETL Testing pipelines
Perform Dashboard and Reporting Validation using real-time business scenarios
Gain hands-on experience through enterprise-level projects from E-commerce, Telecom, and Insurance domains
Prepare for ETL Testing, Data Engineering QA, and Cloud Data Validation interviews with mock interviews and real-time project discussions

Course syllabus:

1: Basics of Data & ETL Foundations

Topics

What is Data?
Types of Data
Structured vs Unstructured Data
OLTP vs OLAP
Data Warehouse Concepts
ETL vs ELT
Data Pipeline Architecture
Source → Staging → Transformation → Warehouse → Reporting Layers
Real-Time ETL Architecture
Batch vs Real-Time Processing
Data Lake vs Data Warehouse
Introduction to Cloud Data Platforms

Hands-On

Understanding raw business datasets
Reading source-to-target mappings
Identifying data flow in real projects

Module 2: SQL for ETL Testing

Topics

SQL Fundamentals
SELECT, WHERE, GROUP BY, HAVING
JOINS
UNION / UNION ALL
Subqueries
CTEs
Window Functions
Aggregate Functions
Date Functions
String Functions
CASE Statements
Stored Procedures
Query Optimization Basics

ETL Validation SQL

Row Count Validation
Duplicate Validation
NULL Validation
Primary Key Validation
Referential Integrity Validation
Data Comparison Techniques
Aggregation Validation
Transformation Validation

Real-Time Scenarios

Product pricing validation
Insurance premium validation
Telecom billing validation

Hands-On

Writing 200+ ETL validation queries
Comparing source and target systems

Module 3: Advanced SQL for Data Warehouse Testing

Topics

Fact Tables
Dimension Tables
Star Schema
Snowflake Schema
Slowly Changing Dimensions (SCD)
SCD Type 1
SCD Type 2
Incremental Load Validation
Surrogate Keys
Data Mart Validation
Historical Data Validation

Hands-On

Testing enterprise warehouse models
Building reconciliation queries

Module 4: Python for ETL Automation

Topics

Python Basics
Variables
Data Types
Loops
Functions
OOP Concepts
File Handling
Exception Handling
Logging
Virtual Environments

Libraries

Pandas
NumPy
OpenPyXL
CSV Handling

Hands-On

Reading CSV, Excel, JSON
Automating validations using Python
Building reusable functions

Module 5: ETL Automation Framework Design

Topics

Automation Framework Architecture
Data-Driven Framework
Config-Based Execution
Dynamic SQL Execution
Metadata-Driven Validation
Reusable Validation Libraries
Logging Framework
Error Handling
Report Generation

Framework Components

Database Connection Utility
Validation Engine
Report Engine
Query Executor
Config Reader

Hands-On Project

Students will build a full ETL automation framework from scratch.

Module 6: Snowflake Testing Complete Coverage

Snowflake Fundamentals

Snowflake Architecture
Virtual Warehouses
Databases & Schemas
Micro-partitions
Time Travel
Cloning
Stages
File Formats

Snowflake Data Loading

COPY INTO
Internal Stage
External Stage
Snowpipe
Data Ingestion Validation

Snowflake ETL Testing

Source to Snowflake Validation
Stage Validation
File Validation
Data Load Validation
Transformation Validation
Warehouse Validation
Performance Validation

Advanced Snowflake Testing

Streams & Tasks
CDC Validation
Incremental Data Validation
Snowflake Query Profiling
Query Performance Analysis
Data Retention Validation
Semi-Structured Data Validation (JSON)

Snowflake Security Testing

Role-Based Access
Masking Policies
Row-Level Security
Data Governance Validation

Hands-On

Real-time Snowflake project
Cloud warehouse validation
Production issue debugging

Module 7: dbt Testing & Transformation Validation

dbt Fundamentals

What is dbt?
dbt Architecture
dbt Workflow
Models
Materializations
Seeds
Snapshots
Macros

dbt Transformation Testing

Source Testing
Model Testing
Schema Testing
Relationship Testing
Custom Tests
Freshness Validation

dbt + Snowflake Integration

Running dbt on Snowflake
Incremental Models
Transformation Lineage
Dependency Graph

Real-Time Validation

Business Rule Validation
Data Contract Testing
End-to-End Transformation Validation

Hands-On

Build dbt models
Validate transformations
Execute automated dbt tests

Module 8: Incremental Load & CDC Testing

Topics

Full Load vs Incremental Load
Watermark Logic
CDC Architecture
Insert/Update/Delete Validation
Late Arriving Data
Historical Data Validation
SCD Type 2 Validation

Hands-On

Validate CDC pipelines
Test incremental logic using SQL & Python

Module 9: PySpark for Big Data Testing

Topics

Spark Architecture
DataFrames
Transformations
Actions
Spark SQL
Partitioning
Distributed Validation

Hands-On

Validating billion-record datasets
Distributed data quality checks

Module 10: Data Quality Engineering

Topics

Data Profiling
Data Quality Dimensions
Accuracy
Completeness
Consistency
Timeliness
Validity

Great Expectations

Schema Validation
Null Checks
Data Profiling
Custom Expectations

Hands-On

Enterprise data quality monitoring

Module 11: Airflow & ETL Pipeline Validation

Topics

Airflow Basics
DAGs
Task Dependencies
Scheduling
Monitoring
Pipeline Failure Validation

Hands-On

Validate ETL workflows
Pipeline dependency testing

Module 12: API & File Validation in ETL

Topics

API Testing Basics
JSON Validation
XML Validation
File-Based ETL Validation
CSV Validation
Excel Validation

Hands-On

Retail product API validation
File reconciliation automation

Module 13: CI/CD for Data Testing

Topics

Git Basics
Jenkins Basics
Automated ETL Validation in CI/CD
Deployment Validation
Smoke Validation

Hands-On

Running automated ETL test suites in pipelines

Module 14: Reporting & Dashboard Validation

Topics

Tableau Validation
Power BI Validation
KPI Validation
Aggregation Testing
Dashboard Reconciliation

Hands-On

Validate business dashboards against warehouse data

Module 15: Real-Time End-to-End Industry Project

Project 1: E-commerce Data Platform

Business Use Case

Validate pricing and inventory data coming from Amazon, Walmart, Target, and Instacart.

Validation Areas

Product pricing validation
SKU matching
Duplicate checks
Incremental load validation
Data freshness checks
CDC validation

Project 2: Insurance Data Warehouse Migration

Business Use Case

Migration from Oracle/Teradata to Snowflake.

Validation Areas

Historical data validation
SCD testing
Fact & dimension validation
Regulatory data checks

Project 3: Telecom Billing Platform

Business Use Case

Validate telecom billing pipelines processing millions of records daily.

Validation Areas

Billing accuracy
Usage aggregation
Customer data reconciliation

Interview Preparation Program

Included

SQL Interview Questions
Python Coding Questions
Snowflake Interview Questions
dbt Interview Questions
ETL Scenario-Based Questions
Mock Interviews
Resume Building
LinkedIn Optimization
Real Project Explanation Training

Student Deliverables

Each student receives:

Complete Notes
SQL Scripts
Python Automation Framework
Snowflake Validation Scripts
dbt Project
Real-Time Project Documents
Interview Question Bank
Resume Templates
Certification

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ETL Testing Automation with Python, Snowflake, dbt & PySpark- Live Training Day2

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