Assess your data sources. Perform a count of records of the primary source table so that you can later confirm that all of the data was moved. Create test cases. Consider testing the data on the target system for completeness and quality. Performance testing of the ETL process is also a good idea. Begin the ETL process with the extraction. Extract data from the source systems. Confirm that all of the data has been extracted properly and completely.
Perform the necessary data transformation. Confirm that the data has been transformed to the appropriate format for the target system and that it complies with business rules.
Load the data into the target destination. Check the record count to confirm that all of the data has been moved. Confirm data integrity by checking to see if any records were rejected by the target system and not loaded.
Document your findings. Please mail your requirement at [email protected] Duration: 1 week to 2 week. ETL Testing Tutorial. Reinforcement Learning.
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Here are the responsibilities which are played by different groups: Business Analyst: Business Analyst gathers and documents the requirements. Infrastructure People: These people set up the test environment.
QA Testers: QA Testers develop test plans and test scripts and then execute these test plan and scripts. Developers: Developers perform the unit test for each module. Database Administrator: Database Administrator test for the performance and also for the stress.
It includes these steps: Design and validation tests Setting up the test environment Executing the validation test Reporting the bugs Tasks performed in ETL Testing Tasks involved in ETL testing are: Understanding of data, used for reporting Data Model Reviewing Mapping of the source to target Checks the data in the source data Validation of packages and schema In the target system, data verification should be done Verification of aggregation rules and data transformation calculation Data comparison between the target system and data source For the target system, quality and data integrity should be examined.
Performance testing of data. Data count verification in the source and target system. ETL testing verifies the transformation, extraction as per requirement and expectation.
ETL testing verifies if table relations join and keys are preservers during the transformation. The operation performed in Database Testing Database testing focuses on data accuracy, the correctness of data, and valid values.
Database testing performs the following operations: Database testing focuses on verification of the column in a table that has valid data values. To verify whether the primary or foreign key is maintained, database testing is used. Database testing verifies if the data is missing in the column.
Here, we check that are there any null values in columns which should have a valid value? We verify the accuracy of data in columns. We will verify the mapping document whether the ETL information provided or not.
Log change should maintain in every mapping doc. We will validate the target and source table structure with the corresponding mapping doc. The data type of source and target table should be the same. ETL testing ensures that the transfer of data from heterogeneous sources to the central data warehouse occurs with strict adherence to transformation rules and is in compliance with all validity checks.
It differs from data reconciliation used in database testing in that ETL testing is applied to data warehouse systems and used to obtain relevant information for analytics and business intelligence. Effective ETL testing detects problems with the source data early on—before it is loaded to the data repository — as well as inconsistencies or ambiguities in business rules intended to guide data transformation and integration.
The process can be broken down into eight stages. ETL testing fits into four general categories: new system testing data obtained from varied sources , migration testing data transferred from source systems to data warehouse , change testing new data added to data warehouse , and report testing validate data, make calculations. Testing during the ETL process can also include user acceptance testing, GUI testing, and application migration tests to ensure the ETL architecture performs well on other platforms.
Incremental ETL tests can verify that new records and updates are processed as expected. Identifying challenges early in the ETL process can prevent bottlenecks and costly delays. Creating a source-to-target mapping document and establishing clear business requirements from the start is essential.
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