Wednesday, 12 April 2017

ETL vs Database Testing

Both  ETL  testing  and  database  testing  involve  data  validation,
but  they  are  not  the same.ETL testing is normally performed on data in a data warehouse system, whereas database testing is commonly performed on transactional systems where the data comes
from different applications into the transactional database.

ETL Testing 
ETL testing involves the following operations:

1.   Validation of data movement from the source to the target system.
2.   Verification of data count in the source and the target system.
3.   Verifying data extraction, transformation as per requirement and expectation.
4.   Verifying  if  table  relations  –  joins  and  keys  –  are  preserved  during  the
transformation.

Common ETL testing tools include QuerySurge, Informatica, etc.

Database Testing 

Database testing stresses more on data accuracy, correctness of data and valid values.
It involves the following operations:

1.   Verifying if primary and foreign keys are maintained.
2.   Verifying if the columns in a table have valid data values.
3.   Verifying  data  accuracy  in  columns.
Example:  Number  of  months  column  shouldn’t have a value greater than 12.
4.   Verifying missing data in columns. Check if there are null columns which actually
should have a valid value.
Common database testing tools include Selenium, QTP, etc.

Difference (Database and ETL)

Primary Goal 
 Data validation and  Integration and ETL -Data Extraction, Transform and
Loading for BI Reporting

Applicable System 
Transactional system where business flow occurs and ETL-System containing historical
data and not in business flow environment

Common tools          
QTP, Selenium, etc.  and ETL -QuerySurge, Informatica, etc.

Business Need 
It is used to integrate data  from multiple applications,  Severe impact. and ETL It is used for Analytical  Reporting, information and  forecasting.

Modeling 
 ER method and ETL -Multidimensional

Database Type 
 It is normally used in OLTP  systems and ETL It is applied to OLAP systems

Data Type 

Normalized data with more  joins and ETL -De-normalized data with less joins, more indexes, and  aggregations. 

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