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Volodymyr Klymenko, CEO, Senior Automation Quality Assurance engineer

Nov 26, 2023 15 min read

Data Migration Testing: Advanced Approaches and Best Practices

This article is a comprehensive guide that covers the best practices and methodologies for effectively testing data migration. It delves into the crucial steps of planning and executing data migration tests, offering practical insights and real-world case studies. Additionally, the article provides valuable resources in the form of templates and examples for data migration test cases, making it a practical tool for IT professionals involved in data management and migration processes. This guide aims to ensure smooth, error-free data migration through thorough and standardized testing techniques.

Data Migration Testing
Moving software to another server, changing technology, upgrading to a new version, or migrating to another database are common steps in software development and significant challenges for the testing team. Are you wondering how to do data migration testing? What exactly does this process mean, and what are the requirements for specialists in these situations? Stay tuned to find the answers to all these questions.

What is Data Migration?

Data migration is transitioning from one system state to another to improve performance, security, or availability. However, this process can lead to unforeseen consequences, such as data loss, application interruptions, or incorrect integration of new components. This is where the testing team will play an important role. Their task is to ensure the application works stably, efficiently, and safely despite all the changes and challenges associated with data migration and check the data integrity. In this article, we will analyze all the features of migration data testing and give you a list of valuable tips. Let's start! 

Different Types Of Data Migration 

Correct data migration is crucial in developing and maintaining applications and databases. In this article, we will look at the main types of data migration. Each of these types of migration has its characteristics and advantages. 

Application Migration 

Application migration is moving an entire application from one environment or platform to another. This type of migration can lead to numerous benefits: 

  1. Cost reduction: Migrating to a new platform can reduce application operation and maintenance costs by optimizing resources and reducing infrastructure costs. 
  2. Reduction of dependence: Migration reduces the program’s dependence on specific systems or platforms, making it more universal and independent. 
  3. Risks and security: Adopting new technologies can eliminate or reduce business risks and increase application security. 
  4. Performance improvement: A new environment can improve the performance of the application and its capabilities. 
  5. Improved technical support: Migration can simplify the process of technical support and application administration, which reduces maintenance time and costs. 
  6. Technological changes: Migration can include transitioning to modern technologies improving the program’s competitiveness.
     

Example: There is a need for cloud database migration testing when transferring an application to a cloud platform or switching from ASP technology to ASP.Net. 

Database Migration 

Database migration is transferring all data from one database to another. This type of migration requires the application to be stable and the data in the database to be correct and valid. Database migration testing is one of the key tasks of testers in this field. 

Database migration can lead to product benefits:

  1. Data preservation: Data remains accessible and intact after migration. 
  2. Improved efficiency: Migration can improve the efficiency of working with the database. 
  3. Data improvement: Migration involves analyzing data to enhance its quality by removing superfluous or invalid entries and correcting any corrupted information.

Example: Moving from a relational database management system (RDBMS) to a non-relational one (such as MongoDB). 

How to Test Database Migration? 

Implementing the testing involves establishing a step-by-step procedure to verify the migration's success. This includes validating schema conversions, data completeness, data integrity, data format retention, and ensuring that stored procedures and triggers function correctly post-migration. 

  • Database Migration Test Plan: Begin with a comprehensive test plan tailored specifically for the migration process. This plan should define the objectives, scope, resource allocation, timeline, and deliverables. It must also include risk assessment and contingency plans for potential migration challenges. 
  • Database Migration Testing Checklist: A meticulously crafted checklist is vital to ensure no aspect of the migration is overlooked. This checklist should cover pre-migration, during-migration, and post-migration testing phases, encompassing data integrity, data transformation rules, application dependencies, and performance benchmarks. 
  • Database Migration Testing Strategy: The testing strategy should encompass the overall approach to testing, including the methodologies to be used (such as white box, black box, or gray box testing), the type of tests to be conducted (like unit, system, and acceptance tests), and the criteria for test pass or fail outcomes.  
  • Test Cases for Database Migration: Develop specific test cases that will validate every critical aspect of the database migration. These test cases should cover the validation of data mapping, successful data loading, and the application's functionality after the migration. 

This section should serve as a blueprint for ensuring a seamless and secure transition during database migrations, providing a structured and systematic approach to testing and validating each stage of the migration process. 

Migration to a New Server 

Server migration is a migration in which data is moved from one physical server to another. This process includes not only the data itself but also the server configuration transferred to the new object. This approach achieves several important advantages depending on the new server: 

  1. Advanced configurations: A new server enables enhanced outcomes, including cost optimization, improved performance, and the availability of essential features that the old one lacked. 
  2. Improved reliability: A new server usually means improved reliability and stability, helping to avoid crashes and downtime.
  3. Greater clarity in logs: A new server can provide more detail, making it easier to analyze request responses between system components and identify problems. 
  4. Increased performance: The migration process, combined with the adoption of more powerful hardware, leads to a notable increase in system performance.

Example: Moving from a Windows operating system to a mainframe server or from an HP Box to an IBM Box. 

These main types of data migration reflect the important role of information movement processes in supporting the effective functioning and development of information systems and computer environments. 

Why Do You Need Data Migration Testing?

Program migration to a new system can occur for various reasons, such as system consolidation, outdated technology, optimization, or other circumstances. It is indisputable that no matter how professional the developers are, this process is challenging and full of potential problems. Testing of the migration process is critical to ensure the following: 

01

Ensuring the highest comfort for users: During the migration, it is crucial to avoid any obstacles or inconveniences for the end users. This includes avoiding downtime and data loss. 

02

Retention of functionality: It's essential to guarantee that all the software features remain available to the users during the migration and that no unanticipated change or removal of features occurs.

03

Compatibility: Ensuring a program is compatible with all hardware and software is crucial. This includes support for legacy hardware and software platforms and testing for compatibility with new platforms.

04

System performance: Data center migration testing also aims to ensure that the system response time of the application is the same or even better than the previous environment.

05

Ensuring connection reliability: During testing, it's imperative to verify that connections between servers, hardware, and software are seamless, ensuring an uninterrupted flow of data.

Testing of data migration prevents possible errors that may occur during this process. 

When to Implement Data Migration Testing? 

Data migration testing should be carried out at the following stages: 

  1. Pre-migration testing:Check the status of the old system and prepare it for migration. 
  2. Migration testing: Evaluation of the data migration process and its integrity. 
  3. Post-migration testing: Check the new system’s operation after the migration.

It is also important to consider these types of testing when testing data migration:

Backward compatibility check: 

  • Confidence in the correct data transfer and settings from the old system to the new one. 
  • Check the smooth migration of the application of the previous two versions without any problems.

Rollback testing:

  • Creation of scenarios simulating possible errors and inconsistencies during data migration. 
  • Checking the correctness and effectiveness of the rollback mechanism after the occurrence of errors. 
  • Fixing the time required to restore the system to the previous version and marking it in the test results for further analysis. 
  • Run automated regression testing to ensure that the migration has not adversely affected the system and that the rollback works successfully.

Before starting data migration testing, it is important to study the system changes and migration strategy in detail and how to write test cases for data migration effectively. 

Different Phases of Migration Testing 

Migration testing is a critical element of successful data migration and includes three main phases: 

Pre-Migration Testing 

At this stage, the initial state of the old system and its preparation for migration are studied. The main tasks include: 

  • Checking data for errors and inaccuracies. 
  • Checking the availability of necessary backup copies and recovery tools. 
  • Development of data migration test cases.

Migration Testing 

This phase evaluates the quality and correctness of the data transfer process. Important aspects include: 

  • Tracking the migration process and identifying possible problems. 
  • Data integrity checks after migration. 
  • Testing the functionality of the new system for defects.

Post-Migration Testing 

At the final stage, the stability and performance of the new system are checked after the migration is completed. This includes the following tasks: 

  • Checking whether the new system works correctly in real production conditions. 
  • Identification and correction of possible defects that may occur after migration. 
  • Tracking the performance and speed of the new system.

Migration testing aims to ensure smooth and successful data migration, reducing risks and minimizing possible negative business consequences. 

Data Migration Test Strategy 

Creating an effective testing strategy for data migration is a critical task aimed at minimizing risks and ensuring a successful migration. Here are the critical steps of this strategy: 

01

Formation of a specialized team: First, forming a team of developers and testers with the specific experience and knowledge necessary for this process is crucial.

02

Business risk analysis and identification of possible errors: It is important to conduct a business risk analysis with various stakeholders, including the project manager, business analyst, architect, product owner, and business owner. This will help identify possible risks and develop tests to detect them.

03

Analyze and define the scope of the migration: Establish a clear scope of testing by determining what needs to be tested during the migration. Prepare a data migration test plan and data migration testing checklist.

04

Choosing the right tool for the migration: Determine whether the testing will be automated or manual and choose the right tools, such as automated tools for comparing source and target data.

05

Create an appropriate test environment for the migration: Make sure there are separate test environments for testing before and after the migration. Document the technical aspects of both migration systems in detail to set up your test environment properly. 

06

Data migration test specification preparation and review: Prepare a Migration Test Specification document that details all aspects of testing, including methods, number of test cycles, schedule, test environment, and tester qualification requirements. Conduct a review of this document with all stakeholders. 

07

System launch after migration: After successfully testing the system, document and complete the production migration task list to ensure the system is up and running in the new environment.

This strategy will help ensure the data migration is performed without errors and risks and meets the organization's business needs (you can also read about our software testing for marketplace platforms and healthcare testing services).

Challenges in Data Migration Testing 

Problems in data migration testing require special attention and an effective solution. Below are some of the main issues that arise during this process:

Data quality 

One of the main problems is poor data quality, which can result from improper collection, processing, and storage of information. This can include incorrect records, duplication, archaic data formats, and sometimes even loss of data integrity during transmission or storage. To solve this problem, you must use data validation and cleaning methods before migration and develop strategies to improve data quality in the new system. 

Data inconsistency 

During the migration, data from the old system may not be appropriate for the new system. This may be due to changes in the database structure, data types, or information processing scheme. To overcome this problem, conducting a detailed data structure analysis and developing a transformation map must ensure that the data meets the new requirements. 

Data loss 

The risk of data loss during migration is always present, especially for mandatory fields. In case of loss of this data, there may be a significant problem as it cannot be recovered. To prevent this, it is important to ensure data is backed up before migration and carefully monitor the process to avoid loss. 

Data volume 

The large amount of data that needs to be migrated can lead to significant challenges. For example, there may be hundreds of thousands of subscribers in the telecommunications industry, each with a large amount of data. You can use specialized tools and develop migration optimization strategies to automate this process. 

Simulation of the environment in real-time 

Simulating a real environment during testing is critical. Given actual data and a real system, testers can spot problems that might go unnoticed in an isolated lab environment. This requires access to data from real sources, replicating the environment, and identifying the volume of data to migrate. 

Modeling the amount of data 

Thorough analysis and modeling of the volume of data is a crucial stage in migration planning. Given the specificity of the data, teams must develop a strategy for processing and moving the volume of information, which can be significant. Automation and extrapolation are helpful tools for managing large volumes of data during migration. 

The ultimate goal of data migration testing is to ensure that no data is lost during the migration and that the product runs smoothly, which requires detailed planning, analysis, and testing. 

Tips for Minimizing Risks During Data Migration

Data migration is a technically demanding process, and the success of this task can significantly affect the functionality of the new system. These tips are helpful not only for testers involved in this process but, most importantly, for developers. Here are detailed tips to help you improve the testing process:  

Data standardization before migration 

  • Before migrating, determine the data formats and their structure in legacy systems. 
  • Make sure the data meets the modern standards used in the new system.

Example: If you have a database with dates in different formats (e.g., MM/DD/YYYY and DD-MM-YYYY), convert them to a single standard to avoid migration conflicts. 

Improving data quality 

  • Use data cleansing and validation tools to detect and correct errors. 
  • Develop a policy for handling duplicates and resolving data conflicts. 
  • Perform a database audit and remove redundant or unnecessary data. 
  • Plan the transfer of data to the new system in such a way as to avoid contamination of the new database with redundant records.

Example: If you are migrating a customer database, check if inactive subscribers can be deleted, merge all duplicates and fix all corrupted data. 

Rechecking restrictions and procedures

  • Verify that all database constraints (e.g., keys, uniqueness constraints) meet the new system's requirements. 
  • Validate complex queries and create test scripts to validate them in a new environment. 

Example: If your old system uses uniqueness constraints to identify users, ensure uniqueness is preserved during the migration.

Choosing the right data migration testing tools 

Consider test automation tools that can help you perform data validation on your new system.

Example: Using automated test scripts allow to speed up testing and free ap tester’s time. 

Data quality is a critical factor in completing data migration testing. So, following these tips, you will achieve the desired result. 

Conclusions 

In this article, we have delved into the core essence of data migration, described the types of this process, and emphasized the importance of testing at all stages of migration. We also discussed when data migration becomes necessary and highlighted the key steps involved in the process. You have received a list of difficulties that may arise during data migration and tips for further solving and avoiding them. May this newfound knowledge contribute to the seamless execution of data migration tasks in your projects! You can also read about our fintech testing services to gain insights into how we ensure the quality, security, and compliance of financial technology applications and systems. Reach out to us today to discuss your project requirements and let our expert team ensure the quality and security of your software products.

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Frequently Asked Questions

What are the possible risks associated with data migration?

Data migration can lead to risks such as data loss, integrity violations, and time and resource costs. Testing helps identify and reduce these risks. 

How do we determine the volume and composition of test data for migration?

The amount of test data for migration should be determined after improving and cleaning of all database and should be documented to check that all data has successfully migrated to a new system. Test data composition should reflect different data types and usage scenarios to ensure comprehensive testing. 

Why is data migration testing important when transitioning to a new system?

Data migration testing is important to avoid data loss, migration errors, and compromised data integrity and security. This process helps ensure business continuity and prevent potential post-migration issues. 

What are the main differences between data migration testing and other types of testing?

Migration testing is akin to cross-platform testing, but focuses on data integrity rather than functionality. It ensures data consistency across different platforms, while most other methods are confined to a single platform. Migration testing uniquely verifies the integrity of actual data, an aspect often overlooked by other testing types that assess the application's functionality and user interface using synthetic data.

How is data integrity checked during migration testing?

Data integrity testing is typically done by checking checksum hash functions and comparing data before and after migration. This helps detect any changes or data loss. 

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