What is data completeness in ETL Testing?
Checking and validating the counts and the actual data between the source and the target for columns without transformations or with simple transformations. …
What is data completeness?
Completeness. “Completeness” refers to how comprehensive the information is. When looking at data completeness, think about whether all of the data you need is available; you might need a customer’s first and last name, but the middle initial may be optional. If information is incomplete, it might be unusable.
Does ETL Testing have scope?
Q. 7 Does ETL Testing have scope? Yes, ETL Testing has scope of the complete ETL process. ETL testing involves testing the ETL procedure in all stages of ETL i.e. pre-ETL or extract stage, during Transform stage and final or load stage.
What do you need to know about ETL testing?
ETL Testing – Data Completeness. Checking Data Completeness is done to verify that the data in the target system is as per expectation after loading. Checking and validating the counts and the actual data between the source and the target for columns without transformations or with simple transformations.
How to compare ETL test results with target table?
Apply transformations on the data using SQL or a procedural language such as PLSQL to reflect the ETL transformation logic. Compare the results of the transformed test data with the data in the target table. The advantage with this approach is that the test can be rerun easily on a larger source data.
What’s the purpose of metadata testing in ETL?
The purpose of Metadata Testing is to verify that the table definitions conform to the data model and application design specifications. Verify that the table and column data type definitions are as per the data model design specifications. Example: Data Model column data type is NUMBER but the database column data type is STRING (or VARCHAR).
How to test ETL with sample queries in SQL?
The steps to be followed are listed below: Review the source to target mapping design document to understand the transformation design. Apply transformations on the data using SQL or a procedural language such as PLSQL to reflect the ETL transformation logic. Compare the results of the transformed test data with the data in the target table.