ETL Testing – Data Warehouse & Pipeline Validation

Module 1: Introduction to ETL Testing

• ETL testing fundamentals: why data quality and pipeline validation matter
• ETL testing vs application testing: key differences and scope
• ETL testing types: source-to-target, data transformation, and reconciliation
• ETL testing lifecycle: requirements, test design, execution, and sign-off

Module 2: Data Warehouse Concepts for Testers

• Data warehouse architecture: staging, ODS, and data mart layers
• Star schema and snowflake schema: fact and dimension tables
• ETL pipeline stages: extract, transform, and load explained
• Slowly Changing Dimensions (SCD): Type 1, Type 2, and Type 3 testing

Module 3: Source-to-Target Testing

• Mapping document: reading and validating ETL source-to-target specs
• Column-level mapping validation: data type, length, and format checks
• Null handling: testing NULL propagation and default value rules
• Completeness testing: row count reconciliation between source and target

Module 4: Data Transformation Testing

• Business rule validation: verifying transformation logic correctness
• Derived column testing: calculated fields and formula validation
• Lookup and reference data testing: join validation and key integrity
• Aggregation testing: SUM, COUNT, and GROUP BY result verification

Module 5: SQL for ETL Testing

• SQL queries for data validation: SELECT, JOIN, GROUP BY, and HAVING
• Minus/EXCEPT queries: identifying mismatches between source and target
• Aggregate comparison queries: reconciling totals across environments
• Writing reusable SQL test scripts for repeated validation

Module 6: ETL Testing Tools

• QuerySurge: automated ETL testing with source-to-target comparison
• Informatica Data Validation Option: rule-based ETL test automation
• Great Expectations: Python-based data quality validation framework
• SSDT and SQL unit tests: database project testing in Visual Studio

Module 7: Performance and Incremental Load Testing

• ETL performance testing: measuring load duration and throughput
• Incremental load testing: delta and CDC-based ETL validation
• Rejection and error handling: testing ETL error tables and logs
• Regression testing: validating ETL after schema and logic changes

Module 8: Capstone Project and Assessment

• Source-to-target test cases: mapping-based completeness and accuracy tests
• SQL test scripts: transformation and aggregation validation queries
• QuerySurge or Great Expectations automated validation project
• Final assessment and course certification

Experience in the Industry Learn from ETL and data warehouse testers who have validated data pipelines for financial reporting, business intelligence, and regulatory compliance projects at banks, insurance companies, and retail organisations.

Backgrounds at the Top Our ETL testing trainers have worked at data warehousing projects and BI testing teams validating Informatica, SSIS, Ab Initio, and cloud-based ETL pipelines for data accuracy and regulatory requirements.

Clear & Effective Teaching ETL testing lifecycle, source-to-target validation, transformation testing, SQL for data testing, SCD testing, ETL testing tools, performance testing, incremental load testing, and regression testing are explained with real DWH project examples.

Hands-On Learning Focus Students write SQL validation queries, build source-to-target test cases, validate transformations, test SCD logic, use QuerySurge or Great Expectations, and execute ETL regression tests through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest ETL testing tools, data quality frameworks like Great Expectations and dbt tests, cloud data pipeline testing on Snowflake and BigQuery, and evolving data testing best practices.