Difference between ETL and Database Testing

In the dynamic world of data management, ensuring data integrity and accuracy is paramount. This is where ETL (Extract, Transform, Load) and database testing come into play. While both processes are crucial for maintaining data quality, they serve distinct purposes and cater to different aspects of the data lifecycle.

ETL testing applies to Data Warehouses or Data integration projects while Database Testing applies to any database holding data (typically transaction systems). Here are the high level tests done in each:

ETL Testing: The primary goal is to check if the data moved properly as expected.

Database Testing: The primary goal is to check if the data is following the rules/standards defined in the Data Model.

ETL Testing  vs. Database Testing:

ETL Testing : 

ETL testing focuses on validating the data movement process within data warehouses or data integration projects. Its primary objective is to ensure that data is extracted, transformed, and loaded accurately and as intended. This involves a series of checks to verify data integrity, consistency, and adherence to business rules.

• Verify that the counts in the source and target are matching.
• Verify that the data is matching between source and target.
• Verify that the transformed data is as per expectation.
• Verify that the data is incrementally getting updated.
• Verify that the foreign-primary key relations are preserved during the ETL.
• Verify if there are any duplicates in the loaded data.

Key ETL Testing Procedures:

1. Count Matching: Verify that the number of records in the source and target systems match.
2. Data Validation: Validate that the data in the target system matches the source system, ensuring data consistency.
3. Transformation Rule Verification: Verify that the transformed data adheres to the defined transformation rules and business logic.
4. Incremental Data Updates: Verify that the data is incrementally updated correctly, reflecting the latest changes in the source system.
5. Foreign Key-Primary Key Relationships: Ensure that foreign key-primary key relationships are maintained during the ETL process, preventing orphan records.

Duplicate Detection: Identify and eliminate any duplicate records in the loaded data.

Database Testing:  Database testing, on the other hand, focuses on verifying that data within a database adheres to the defined data model standards and rules. It ensures that data is accurate, valid, and compliant with the specified data integrity constraints.

• Verify that the foreign-primary key relations are maintained and there are no orphan records.
• Verify that the data in the columns had valid values. eg: If there is a domain (encoded list) defined for a column, check if the values in the column meet that requirement.
• Verify that the data in columns is accurate. eg: If you have an age column, does it have values more than 100.
• Verify if data is missing in columns where required. eg. If is a column is expected to have data always, check if there are any null values.

Key Database Testing Procedures:

1. Foreign Key-Primary Key Relationships: Validate that foreign key-primary key relationships are maintained, preventing data inconsistencies.
2. Data Domain Validation: Verify that data values in columns conform to the defined domains or encoded lists.
3. Data Accuracy Checks: Ensure that data values are accurate and within acceptable ranges. For instance, verifying that an age column does not contain values exceeding 100.
4. Null Value Checks: Identify and address any missing data in columns where data is mandatory.