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   Data Warehouse Testing Categories Data Warehouse Test Data  

Execution-MiH Encyclopedia  →   Enterprise Intelligence  →  SECTION -  Data-Warehouse/Mart  →  CHAPTER -  DW Testing and Implementation  → 

Data Warehouse Test Scenarios

Data Warehouse testing includes all typical testing like exception testing, boundary testing, stress testing etc..

Real Life Simple Scenarios

Simple scenarios are those, which are relatively straightforward and can be the first step to understand the health of the system. examples are:

  • Extraction– Complete table Extraction from a core system with robust DBMS.
  • Transformation – Creation of simple derived attributes (creating complete bill amount from individual billing items) OR creating aggregates.
  • Loading – Loading a dimension set with lesser attributes and without any Transformation during Loading.
  • OLAP – Testing using 'Basic Functions'

Real Life complex scenarios

  • Extraction – Data Extraction from an excel sheet involving filtering out the customers not matching the standard customer code.
  • Transformation – 'De-Dup', 'Integration'
  • Loading – Loading dimensions with large set of attributes.
  • OLAP – Testing population of OLAP population.

Boundary Testing

These are the conditions, which will test the extreme situations possible to be faced by Data warehouse. For example

  • Extraction – No data in the source system.
  • Transformation – Creating derived attribute with input figure being very large OR very small. (For example a % sales revenue figure for sales of USD 10 out of the total sales of USD one million)

Negative Testing

Checking on how the system handles the negative conditions:

  • Extraction – Wrong OR unexpected data in the table. (For example you place the wrong customer ID format, character fields in what should be numeric etc.)
  • Transformation- having negative sales numbers, age of 200 years etc. This is important, as the transformation logic should not only work on what it wants to do, but what all it could face.
  • Loading – Having wrong data sets. For example having data set of dimension 'location' has two columns less OR not existing OR having null values. There should be some fundamental checks, which need to be run by Loading system before it goes for bulk Loading.
  • OLAP- Users entering wrong formulae.

Full Production Simulation

This can be a full scale parallel testing, but is something more than that. Where-as parallel testing is done in synch with the production, the production simulation does not necessarily have to do the same. One takes the back up of the source systems from an earlier date and runs the complete ETL and 'end user tools' operations to look at the results. This typically is a step before the parallel testing is done. Production simulation is more of a lab test by technology before the system is released to full user view of parallel testing.

 

   Data Warehouse Testing Categories Data Warehouse Test Data  
 
All Topics in: "DW Testing and Implementation" Chapter
 Data Warehouse Testing is Different →  Data Warehouse Testing Categories →  Data Warehouse Test Scenarios →  Data Warehouse Test Data →  Data Warehouse Implementation Deployment → 
 

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CONTENT ZONE
Data-Warehouse/Mart

Featured Pages
Dimensional non Strict Hierarchy
Business Themes+ Data Mart matrix
Back-Room Data Warehouse Metadata
Customer Segmentation Analytics and BI

Make 'Executable' Strategy
Maximize Results
Maximize People
Manage Execution

Featured Pages
Data Mart Business Theme Matrix in DW
Data Management Standards for Data Entities
Customer Data Challenges
Metadata Architecture