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Data Migration Testing

All you should know about Data Migration Testing

The core of every organization is data. Either new or old, organizations look for means to keep the data in consolidated databases and servers. This can be done by successful data migration.

According to Gartner reports, about 80% of projects on data migration do not measure up to standard and take money and time. Although a possible risk, migration of data is unavoidable. The great news about this is that these risks can be reduced if some norms are observed. To start with, let us understand what data migration is all about.


What are data migration and data migration testing?

The process of transferring data from one system to another is known as data migration. This is primarily used when an organization intends to transfer the information to another database, merge the system, upgrade another OS, or scale up to data-intensive applications. Data migration can result in the improvement of various performances prompting quicker transactions. The process can be categorized into 6 significant types:

  • Storage Migration:  Data migration happens from an old storage system to new
  • Business Process Migration: Migration of different methods feasible for the business happens inside computing environments. 
  • Database Migration: Data migration to an upgraded platform
  • Application Migration: An application, is completely migrated from one platform to the other for scalability and better performance.
  • Cloud Migration: Migration of any process, data, or application to a computing platform that is cloud-based  
  • Operating system Migration: Migration includes a change in Operating systems such as MAC, Windows, and Land Linux based on performance and virtualization purposes

On the contrary, data migration testing is the process to confirm if the migrated data match the original and is error-free. Failure in the migration of data can result in the loss of sensitive information, resulting in the breakdown of applications/systems and the repercussions of business setbacks. This can be avoided by end-to-end data migration testing. The testing plays a critical part as well in detecting all the possible risks ahead of time. 

The data utilized for testing contains both old data as well as new data sets. Consistent testing must be done for legacy applications as well as the new applications till it stabilizes.

  • Data migration aims to move the data to another place with the highest accuracy and minimum downtime.
  • Identifying the testing environment, as well as the right tools for migration, are key determining factors for an effective migration
  • It is important to verify whether functional and non-functional aspects are met immediately upon completion of the migration without any modifications in data integrity.
  • Any type of glitches should be eliminated after the testing and must be fixed
  • Data migration testing likewise ensures that all the connections stay intact with the required data inflow

There are three stages of the data migration process:

  1. Pre Migration phase: It has to do with the scope, analysis of needs, dependencies as well as planning. The creation of rigorous testing scenarios is made. The plan matches the requirements of the project and business as a whole as well as technology. The schema of a new application is made. The number of records and tables are recorded which can be calculated once the migration is done. The interface of the new application is carefully studied.
  2. In-Migration phase, the plan is performed with procedures that are customized.
  3. The  Post Migration phase ends the migration after the important data verification as regards completeness, compatibility as well as functionality. The records count, schemas, and tables are verified. Redundancies are removed. Cases of mismatching, integrity and modified formats are as well verified. This comes after step-by-step documentation and reports of version control, summing up a pass or fail result for every scenario, downtime summary, test logs, functionality, data leaks as well as other important details.


Thus, the data migration lifecycle includes a thorough assessment of requirements and end-to-end planning. Then a fool-proof strategy-building method with a viable POC is created. With the generation of automatic data migration test scripts, test scenarios are created. With the final execution, the test environment is set up. Defect analysis is assessed and defects are reduced and fixed with the generation of comprehensive end reports. The migrated data application is ready for going live or for the use of production.


Common practices that should be followed:

  • Make the stakeholders active in the planning stage and map out the needs taking every aspect into consideration
  • Ensure the team prepares well with the migration tool beforehand explaining all the procedures to them well ahead of time.
  • The team should be familiar with the scripts and the back-end tech which will be used for the migration
  • Business risk analysts, owners, test engineers as well as architects of the products must be active in getting all scenarios and considering risks from every aspect
  • Give an in-depth report of the number of selected testings, testing approach, the ability of the group and experience, the procedure selected, and the likes, and make it verified by the stockholders.

Tips for Data Migration

  • Try dummy data transfer to test the migration tool to be sure of its efficiency. This will help in planning effectively giving you the general idea of the work like the amount of data that should be transferred per session or if there is a need for any adjustments.
  • Take a load balance testing to ensure the legacy data and the migrated one is synchronized
  • Ensure beforehand that you have the right data mapping to verify that the migration is successful.
  • This should be followed by checking the validity of the data and functional testing. The current application should be able to make the same features as the legacy in the same or decreased turnaround time.
  • Have a backup ready every time in case the migration is not successful
  • In a situation where the migration is not successful at any time, rollback testing ensures that the functions performed by the legacy system are not affected in any way.
  • Regression test and backward compatibility test provide further checks

Benefits of data migration testing

  • ROI improvement
  • Easy scalability
  • Backup methodology
  • Enhance data integration
  • Legacy system relevant with a new tech stack
  • Promoting efficiency

Cons of data migration testing

  • Compatibility problems
  • Investment in training
  • Data Leaks
  • A longer downtime, which leads to loss
  • Compromised Security 

 Most times, data migration leads to leaks, issues in transferring data that is large, irregularities in data, and quality reduction. It is a time-consuming and costly process. Over 70% of organizations are still afraid of the data migration process and reluctant to do it. Nonetheless, the task can be enhanced by end-to-end testing,  after the data is organized and cleaned.


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