Data migration testing ensures accurate and secure data transfer using data engineering services. By 2026, 78% of companies will use data migration testing services to avoid data loss. A robust data migration testing strategy includes mapping, validation, and reconciliation.
The reasons why businesses decide to migrate data vary: reduce costs, enhance scalability and flexibility, add security, enhance collaborative work, system upgrades, etc. But, whatever the reasons, data migration testing goes well beyond transferring information from one source to another. It is a complex process that requires expertise, skills, and – obviously – the right tools. The bigger the amount of data, the more challenging and complex the data migration testing process.
What is the Data Migration Testing Process?
A data migration testing strategy includes validation testing, which verifies that migrated data matches the source data. After migrating HR records, validation ensures the accuracy and completeness of all employee information in the new system.
In simple terms, data migration testing is a process that consists of moving data, application, and other elements – either manually or automatically using migration tools – from a source platform to the destination one. Usually, legacy systems are either replaced or expanded by new applications that share a common dataset.
The current tendency among businesses is to replace on-premises applications and infrastructures with cloud-based applications and storage. And there are good reasons behind this decision. According to Microsoft Office 365, the benefits of cloud environments ‘stack up’ for small and medium-sized businesses:
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Data Migration Testing plays a crucial role and it’s not risk-free. Migrated data is compared with original data, thus allowing experts to identify any possible discrepancies and fix errors. Leveraging data migration testing services and a robust data migration testing strategy reduces go‑live defects, speeds adoption.
What is Validation Testing?
Data migration testing includes Data Level Validation and Application Level Validation.
Data Level Validation Testing is meant to verify if data has been migrated without discrepancies. There are various levels of verifications: row counts (i.e. number of records to be migrated), data verification (i.e. verify the accuracy of a migrated data sample), and entitlement verification (i.e. the verification of the destination database).
The Application Level Validation Testing implies the verification of the functionality of a migrated sample application to ensure its smooth running with the new database. This process implies various validations: logging in to the new application and verifying a sample data set, logging in to the legacy system and verifying the accounts’ status, verifying customer support access to legacy systems, or ensuring user access to legacy systems if the migration process fails.
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Data Migration Testing: Types and Scenarios
Data management services use data migration testing to cover scenarios such as full, incremental, and parallel migrations. Testing ensures the integrity of customer data after migrating from a legacy CRM system to a cloud-based system, preventing data loss or corruption. These are the most common types of Data Migration Testing Process:
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Application Migration
Data migration testing services verify the functionality of all application data and functions after migration. After migrating a payroll application to the cloud, testing ensures the accuracy and reliability of calculations and reports.
In this case, an entire application needs to be migrated from one platform to a new one, such as migrating ASP.Net to Windows Azure or migrating an app to the cloud. QA and software testing services should include identifying requirements and scope of testing, testing the flow in the legacy application and contrasting it with the new one, and – if necessary – testing the new flow to ensure seamless functionality and performance.
One testing scenario could be this one: migrate an application to new technology.
In this case, experts should make sure that the app works correctly and that the new platform supports all the application’s components without errors (e.g. plug-ins, paths, add-ons, etc.) it is important to make sure not only that old data is retained but also that new data functions correctly on the new platform.
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Server Migration
A data migration testing strategy verifies the integrity and availability of data after a server migration. When migrating files to a new server, testing ensures that all documents open correctly and that access rights are preserved.
In this case, the server data and the configuration are migrated to a new server. Migrating HP Box to IBM Box could be a good example. When performing testing, experts should ensure compliance with the destination server and test data handling. It is important to make sure that there is no data corruption.
Generally, the testing scenarios are:
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Database Migration
Data migration testing services verify the integrity of all tables, records, and relationships after a database migration. When migrating from SQL Server to PostgreSQL, testing ensures that no data is lost or modified.
In this case, all the data in an application’s database is migrated to a new database. For the migration to be successful, it is necessary for the application to be stable and the data to be valid. RDBMS to a new RDBMS or to MongoDB could be two migration examples.
The testing scenarios should include:
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OS Migration
This is probably the most complicated type of migration. When an application needs to be migrated from an operating system to a new one, compatibility risks arise and many components (e.g. interface, configuration, etc.) might require re-designing. Here are some examples: Windows to Linux, migration to Cloud-based VMs, or migrating to SaaS.
Testing should include the analysis of the new OS’s dependencies and see how configuration modifications influence the application. Since flow might vary, Extensive Testing and Compatibility testing are mandatory.
These should be the Testing Scenarios:
The common mistakes in Data Migration Testing Strategy
When data migration testing services, errors such as missing test cases or incorrect mapping are common. Missing edge cases can result in incomplete data transfer, leading to operational outages and compliance issues.
We have already seen that data migration is a complex process prone to mistakes. Undoubtedly, the risk of data corruption and loss increases dramatically during migration. According to Hosting Tribunal, the consequences of data loss can reach a dramatic dimension:
Hence, avoiding data loss during migration plays a crucial role. Here is a list of the most common mistakes business owners make when migrating data:
Not removing legacy structures that are inefficient or obsolete
Before you begin the migration process, it is necessary to examine and decide what source data should be migrated. Migrating duplicates or errors can result in expensive failures. It’s time to get rid of all the junk data.
Insufficient or faulty testing and validation
Thinking that migration will run seamlessly is unrealistic. The only way to achieve your goal and save time and money is by performing testing throughout the entire process.
Lack of expertise
Sometimes, business owners underestimate the complexity of the data migration process. Not to mention the challenges it poses. In order for a data migration process to run well, it is mandatory to count on the expertise of specialists who are able to quickly identify and fix data discrepancies.
Leaving end-users aside
Before beginning a data migration process, there is one question that most business owners forget to ask themselves: ‘Who is going to use this data?’ It is not going to be the IT specialist, that’s for sure. So, it is important to engage all the end-users in the migration process, leveraging automated software testing services and manual testing services to ensure a smooth transition. This will help avoid uncountable support requests and downtimes once the migration is completed.
How can Test Automation benefit Data Migration?
Data analytics services use test automation to speed up data migration, reduce manual errors, and ensure consistency. Automated scripts quickly validate millions of records, saving time and improving accuracy.
When it comes to testing, quality is paramount. But so is speed, especially if you want to reduce risks, identify errors earlier, reduce system downtime, save time and costs, and, thus, speed up your business’ development.
Keep in mind that test automation doesn’t mean you will no longer need a specialist to plan, supervise, and validate the migration process. A tool’s efficiency only goes as far as the expertise of the person using it.
“Data migration testing services are vital to ensuring business continuity. A robust testing strategy ensures data integrity, security, and compliance during migration. By simulating real-world scenarios, we identify issues early, minimize downtime, and ensure a smooth transition, allowing organizations to innovate with confidence.”
Elinext software development expert
Conclusion
Data visualization solutions paired with data migration testing reveal gaps and ensure accuracy. Migrating data to new, more powerful systems allows companies to transform and improve their ecosystems, giving them a competitive advantage. However, the data migration process is complex and error-prone. Only the timely use of the right strategies and tools, as well as the prevention of common mistakes, are the keys to a successful migration.
FAQ
What is data migration testing?
Data migration testing services confirm the accuracy and security of data movement between systems. After migrating customer data to a new CRM system, testing verifies the completeness, accuracy, and absence of data loss, ensuring business continuity.
Why is data migration testing important?
Data migration testing prevents data loss, corruption, and downtime. A bank that used testing services avoided compliance fines by ensuring all transaction records were correctly migrated during the core system upgrade.
What are the main goals of data migration testing?
The main goals are to ensure the integrity, completeness, and security of data. Data migration testing services confirm that all sales data is accurately transferred to the new ERP system, without missing or altered records, ensuring business continuity.
What are common challenges in data migration testing?
Challenges include data matching errors, incomplete test coverage, and legacy system issues. A retailer experienced product code mismatches after migration, highlighting the need for thorough data migration testing.
What is the typical test strategy for data migration?
A typical data migration testing strategy includes planning, matching, validation, and reconciliation. Testers validate a sample of data, compare source and target data, and run automated checks to ensure all records are migrated correctly.
What types of testing are performed in a data migration project?
Types include unit, system, validation, and reconciliation testing. Data migration testing services validate data fields, check relationships, and reconcile the resulting data to ensure the correct migration and that all information is functional.
Can automation be used in data migration testing?
Yes, automation speeds up data migration testing by running scripts to validate large data sets. Automated tools quickly compare millions of records, reducing manual effort and ensuring accuracy during complex migrations.
