ETL Testing
ETL testing refers to the process of validating, verifying, and qualifying data while preventing duplicate records and data loss
It confirms that the data we have extracted, transformed, and loaded has been extracted completely, transferred properly, and loaded into the new system in the correct format
ETL testing helps to identify and present issues with the data quality during the ETL process, such as duplicate or data loss
🔧 Technical Skills
- ETL Tools Knowledge
- Informatica, Talend, Apache Nifi, Microsoft SSIS, DataStage, Pentaho, etc.
- Database/SQL Expertise
- Strong skills in SQL for querying, validating, and comparing data.
- Familiarity with RDBMS like Oracle, SQL Server, MySQL, PostgreSQL, etc.
- Data Warehousing Concepts
- Star/Snowflake schemas, dimensions, facts, slowly changing dimensions (SCD), normalization/denormalization.
- Scripting Languages
- Shell scripting (for Unix/Linux-based systems).
- Python (increasingly used in modern ETL and data validation).
- Data Validation and Reconciliation
- Ability to compare data between source and target systems using automated scripts or SQL queries.
- BI & Reporting Tools (optional but beneficial)
- Power BI, Tableau, or Cognos for testing data visualization accuracy.
🛠️ Testing Skills
- ETL Testing Types
- Data completeness
- Data accuracy
- Data transformation validation
- Data quality testing
- Performance testing (load time, scalability)
- Defect Management Tools
- JIRA, Bugzilla, HP ALM, etc.
- Test Management Tools
- HP ALM, TestRail, Zephyr, or similar.
- Automation Knowledge (optional)
- Automation using Python, Selenium (for BI reports), or Apache Airflow for data pipeline testing.
🧠Soft Skills
- Analytical and Problem-Solving Skills
- Ability to identify data mismatches and root causes effectively.
- Attention to Detail
- Critical when validating large volumes of data.
- Communication Skills
- To interact with data engineers, analysts, and stakeholders.
