Data migration plays a crucial role in helping organizations modernize and maintain their systems. Not only that, it’s a critical building block for the digital transformation undertakings that are sweeping both large and small enterprises. These transformations promise enhanced processes across all areas of business, spanning both internal and external-facing functions. And in the middle of this large undertaking is digitization – converting data from analog to digital formats – and digitalization – leveraging digitization to streamline operational efficiency and enhance value to various stakeholders such as customers and employees. This is precisely why data migration testing is more relevant than ever, and the reason why enterprises should prioritize this area of testing.

Defining Data Migration

Data migration testing is the process of verifying that the data transferred from one system to another is accurate, complete, and secure. It involves testing the migration process to ensure that the data is transferred correctly, and any discrepancies are identified and resolved before the migration is complete. The testing process includes both functional and non-functional testing, and it is usually performed in a test environment that is identical to the production environment.

The data migration testing process is usually performed in several stages, including planning, preparation, execution, and reporting. The planning stage involves defining the testing scope, objectives, and requirements, as well as identifying the test environment and test data. The preparation stage involves setting up the test environment, preparing the test data, and designing the test cases. The execution stage involves running the test cases and verifying that the data is migrated correctly. The reporting stage involves documenting the test results and communicating any issues or concerns to the relevant stakeholders.

Examples of data migration testing include verifying the data integrity when moving data from old CRM or ERP systems to new ones, consolidating data from legacy systems, transferring data to the cloud, and simply migrating data from content management systems, e-commerce platforms, and financial systems. These migrations require careful planning, preparation, and execution to ensure that the data is transferred accurately, completely, and securely. While performing data migration, testing plays an equally important role as it helps to mitigate the risks associated with inaccurate, incomplete, and insecure data migration across systems.

Types of Data Migration Testing

Data migration testing is a complex process that involves multiple stages or phases of testing. Various types of data migration testing ensures that the data migration is thoroughly tested end-to-end and any potential flaws in the system are identified and addressed. Let’s look at a few types of data migration testing that testers typically perform throughout the software release lifecycle.

Schema Comparison Testing

Schema comparison testing ensures that the data model or schema structure is matching between the source and target systems. To validate the schema comparison test, users can query the metadata tables easily. Furthermore, the table and column names should match between the source and target systems. Data type mapping between the source and destination should be correct. For instance, the source columns with INT datatype should be NUMERIC in the target system. Lastly, it’s crucial to verify the views, primary keys, and indexes to ensure they are matching.

Data Comparison Testing

Data comparison testing is crucial in ensuring the success of data migration. It involves comparing the data in all the tables, row by row and column by column to certify that the migration was successful. For instance, the tester can check whether the first name column in the source and the target are the same. Additionally, they can also ensure that the date value is matching even though the format might be different between the source and the target. By performing these checks, testers can ensure that the data is accurate and consistent across systems, enabling businesses to make informed decisions based on reliable data.

Data Aggregation Testing

Data aggregation testing involves performing aggregated checks for high volume tables between the source and target systems to ensure data consistency. This is important as comparing data row by row for tables with billions of rows can be time-consuming and expensive. The test verifies that the summation for all numeric columns in a table matches between the source and target systems.

Row Count Testing

Row count testing is one of the simplest and most basic types of tests used in data testing. It ensures that the number of rows between the source and target tables is the same. Row count tests can be performed at different stages of the data load, including the initial load and delta loads. This test helps to identify any issues that may arise due to data loss during the migration or transformation process. By conducting row count testing, testers can be certain that the data is being correctly loaded into the target system.

Unstructured Data Testing

Unstructured data testing involves the testing of data migration from sources such as spreadsheets, PDFs, and documents. Compared with structured data testing, unstructured data testing requires special testing procedures. To start, both NLP and AI algorithms can be used to convert the unstructured data into structured data. A framework can be built for users to input file samples and train the model to be able to add new files for testing. The converted data can then be saved into a database for retrieval during testing. Once the unstructured data has been converted into structured data, the same validations applicable for structured data testing can be performed.

How AI and Automation Can Help

Stay tuned for part 2 of this introduction series on data migration testing for ways that AI and automation can streamline and futureproof the testing process. Or simply schedule a demo below to see data migration testing in action on the AquilaTest test automation platform.