How Effective Is The Data Migration Testing?

Data holds high importance for any application. Maintaining the integrity and quality of data is vital, and hence any change with respect to data needs a formal strategy for testing. The strategy would depend on factors such as time availability, critical of data, budget, etc. and would need to be personalized according to the application. 

What is Data Migration?

Data migration as the name suggests is moving the data from one environment or system to another. There can be various reasons for migrating the data such as upgrading the existing version of the database, relocation of servers, upgrades in servers, etc. 

This can be a cumbersome task for a complex application having large volumes of data. To craft an appropriate testing strategy, the scope of the project needs to be analyzed wisely. Data migration projects are carried out in a big bang approach as well as in an incremental fashion. 

Risks and challenges associated with the data migration process

Loss of data: Any amount of data loss can have a huge impact on the reputation of the company. 

Data corruption: Data is likely to get corrupted during migration. The target database may allow the loading of corrupt data, but after that, the application is likely to get a crash. 

Prolonged downtime: If the migration takes a long time, it might increase the downtime of the application, affecting the business of the application

Orchestration risks: The dependence between the several objects has to be measured while migrating the data. In order to migrate a person’s details, it is important first to migrate the person object. The order in which migration activities take place is necessary to be maintained. 

Semantics Risks: They are the faults that occur if the units for some fields are not same in target databases and source. 

Operational risks: If the source database is used at the time of data migration, the data might change that can lead to inconsistency of data in the target database.

Assessing the existing data: Quality and other existing issues need to be assessed in the existing data. There can be drawbacks if the behavior and nature of the existing data are ambiguous or unknown beforehand.

Target parameterized risks: They arise while the target application experience a change during migration and it becomes conflicting to the migrated data. 

Data Migration Testing: The Solution

Database migration testing is required while moving data from the old database to a new one. The old database is also known as the source database or the legacy database, and the new database is referred to as the destination or target database. Data migration can be done manually but is preferable to choose an automated ETL process (Extract-Transform-Load) to move the data. Moreover, for mapping the old data structure to the new one, ETL tool is incorporated in various business rules to increase the quality of data moved to the target database. 

Usually, data migration testing is of two types:

• Data Validation
• Migration Run Tests

Data Validation Tests:

It aims to:

1. Check integrity and completeness of data
2. Verify application stability
3. Identify semantic errors
4. Verify the interfaces with application
5. Perform appearance tests which select business critical objects for testing after consultation with business experts.

Migration Run Tests:

They help in validating every aspect of the migration program from extract, filter and transform, and load to the target.

They can be:

• Complete Migration Tests: They run using the complete data set and help find out the appropriate time required for migration.
  
• Partial Migration Run Tests: They assist in speeding up the development cycles and used to guess the expected execution time which might or might not be accurate.