ETL is normally used to the Extract, Transform and Load Data in Data Integration and Data Warehousing projects. While there clearly defined the methodologies and it’s an best practices for the designing and development of ETL process, there is a very little literature in the ETL Testing. Transaction Processing and ETL Testing it’s different because of ETL Testing is a data centrist which contains comparing the large volumes of data over the heterogeneous data sources.We describe the brief out of the ETL Testing strategies:
- Data Accuracy : Ensure the data it’s to be loaded accurately and then transformations are working in the expected form.
- Data Completeness : Verify the all expected data it’s loaded
- Data Integrity : Ensure the data relations a has been maintained as an expected
Data Accuracy Testing
Data Accuracy Testing it’s ensures the data is exactly transformed and it’s loaded as right a way. Some of the issues that could be identified with this testings :
- Truncation of a characters due to the column size of data type mismatch
- Columns are Wrong mapped of in the ETL Testing. Which may be result in the wrong data or nulls getting the populated in target
- Errors during the transformation logic implementation may the result will be bad data in target table
Data Completeness Testing
The purpose of the Data Completeness testing are to verify the that all expected data is clearly loaded in the target source. Some of tests it will be runs are :
- A simple Data Completeness check form it’s verify that an counts of the rows in a driving table form of source is matched with the counting into the target table
- A better testing is to be compared to the counting of non-null data in the each column of source and target tables
For important columns, that comparing list of duplicate values and in the counting of rows for the each distinct value it’s might be useful
- ETL Performing the completeness checks transformed columns is an tricky, but could be done most of the timing but understand the transformation rules, then comparing counts of expected results
Data Integrity Testing
- Check for the counts of ‘unmatched’ or ‘unspecified’ rows in fact to be an dimension foreign keys and it’s comparing the % of foreign key matching expects by the running queries in the source data and target data
- For the sample data, verify the facts recordings it’s mapped to correct dimension recording using logical key and surrogate key from the source.
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