The amount of data produced on a daily basis is staggering. But all the data in the world doesn’t do much good if it can’t flow between systems, applications, and infrastructures. This is why the process of data migration is mission-critical for data-driven organizations—and data migration testing, doubly so.
But to understand why, we must first define what data migration testing is, what makes it more important than ever, why manual testing just can’t cut it anymore, and how automation alone may not be enough to keep data-driven organizations in business.
What is data migration testing?
Data migration testing is a critical process that ensures the successful transfer of different types of data from one system to another. Use cases that benefit from this testing range from simple (ensuring the success of a basic database migration) to complicated (transferring data from a legacy system to modern systems that are compatible with new applications).
In modern data environments, anything that can go wrong usually tries to. So the process of data migration testing is essential for maintaining data integrity, minimizing disruptions, and ensuring that functional and non-functional requirements are all met when the migration itself is complete.
As such, data migration testing typically involves three main testing phases:
1. Pre-migration testing: Before the process begins, the testing team aligns on the scope of data that will be moved, performing data mapping between where the data currently resides and its target location. This phase is vital for understanding the data schema of the new system in play, as it allows the team to identify risks and create contingency plans as part of a clear migration plan and strategy.
2. Migration testing: As the digital journey begins, the testing team will monitor the process in real time—sampling and validating the data as it migrates while testing the integration of the target system with other systems. Doing so ensures the data is transferred correctly and any discrepancies are identified.
3. Post-migration testing: Once the migration is complete, the team will compare the data in the target system to its source, confirming that everything has transferred correctly and that optimal data quality was maintained. Additionally, post-migration testing often involves data reconciliation and testing the functionality, performance, and documentation of the system the data migrated to.
The overall process will utilize a combination of white-box and black-box testing methodologies. At their most basic, white-box testing ensures that the migration logic is correctly implemented, while black-box testing verifies that the migration meets the functional and business requirements. Both are essential aspects of a testing team’s comprehensive data migration testing strategy.
Why data migration testing is more important than ever
As more business organizations aspire to be data-driven, more data is created. And all of that data has to migrate—across systems, departments, organizations, and continents. No wonder, then, that data migration testing is becoming mission-critical for modern organizations. And this involves several specific challenges.
In addition to its ever-increasing volume, the data itself is becoming more complicated. New technologies are emerging. Organizations continue to merge or acquire other companies. Companies themselves are constantly updating and optimizing existing systems. This all creates a dynamic, turbulent landscape that data migration needs to navigate while testing teams work to make sure data losses or corruption don’t occur along the way.
The rules required to navigate this landscape are growing more complex as well. Stringent data protection regulations, like Europe’s General Data Protection Regulation (GDPR), have increasingly global implications as the infrastructure connecting individuals and businesses around the world grows more sophisticated. Data migration testing processes now help verify that migrated data adheres to these regulations, helping to minimize the risk of non-compliance and potential (very expensive) legal penalties.
Other complications stem from digital transformation initiatives as organizations migrate data to new applications in order to utilize advanced analytics, artificial intelligence (AI), and machine learning capabilities. Migration testing is also critical as the digital transformation process takes place. Without testing, these initiatives could easily result in disruptions to business operations or irreparable damage to organizational data quality, undermining the desired effects of the initiative itself.
Similarly, the infrastructures that enable organizations to be data-driven require frequent system upgrades and the ongoing integration of disparate systems. Teams use data migration testing to ensure system compatibility and a seamless flow of data across the organization’s technological ecosystem. This supports the efficient operations, strategic initiatives, and data integrity that being data-driven requires.
Given these compounding complications, data migration testing is quickly becoming more than just another tool in the data team toolbox. Businesses are embracing migration testing as a critical component of modern organizational strategy—supporting operational excellence, compliance, and strategic decision-making, all of which are essential for staying competitive in today's whipsawing data-driven business environment.
Checking the boxes with data migration testing
While data migration testing is employed to help with an increasing variety of data-related issues and concerns, the process used in a testing environment tends to be fairly consistent.
As part of a data migration testing strategy, these steps can be followed in the form of a checklist:
1. Planning for the pre-migration phase
- Objective definition: Always clarify the goals of each data migration, identifying any needs specific to data consolidation, potential system upgrades, or cloud migration, for instance. All relevant stakeholders should be engaged while objectives are being defined. This ensures all business needs are captured and will be accounted for.
- Source data analysis: Utilize data profiling tools to assess the quality, volume, and structure of the data that will be migrated.
- Data cleansing strategy: Plan for deduplication, normalization, and correction of any source data anomalies.
- Data mapping: Employ data mapping tools to outline field-level transformations and ensure alignment with the target schema. To mitigate risks of misinterpretation or oversight, consider conducting an iterative review and validation of your mapping logic with stakeholders engaged while defining migration objectives.
- Establish KPIs: Define specific, measurable outcomes and related metrics for data quality, migration time, and post-migration system performance.
- Backup verification: Once pre-migration planning is complete, implement checksums or hash totals that can be used to verify the completeness and integrity of the backup data.
2. Testing environment setup
- Production environment replication: Use virtualization or cloud environments to mirror the production setup closely.
- Isolation measures: Ensure network and access configurations prevent any interference with live environments.
- Plan for updates: Consider establishing a process for regularly syncing the test environment with production updates. Doing so reduces risks associated with environmental drift, as testing will always be conducted on the most current setup.
3. Data migration tool configuration
- Tool selection: Choose migration tools based on compatibility with source and target systems and overall data complexity.
- Parameter configuration: Set up conversion rules, connection strings, and error handling procedures.
4. Perform a trial migration
- Pilot testing: Select a representative data subset for trial migrations to identify patterns of errors or issues.
- Test iteration: In some cases, iterative trial migrations—increasing volumes of data over time—can help teams understand performance impacts and potential bottlenecks at scale. Establishing specific criteria or thresholds for escalating trial migration volumes can ensure these testing phases are systematic and controlled.
5. Execute the data migration
- Real-time monitoring: Employ monitoring tools to track the migration process, focusing on error rates, throughput, and performance metrics.
- Preemptive planning: By establishing a rollback or remediation plan, teams can be prepared if unforeseen and significant issues (e.g., data loss or corruption, performance degradation) arise during the migration process.
6. Implement post-migration testing
- Data integrity and quality checks: Apply SQL queries or scripts to compare source and target datasets for discrepancies. Implement automated data quality tools to assess data accuracy, validity, and consistency post-migration.
- Data verification and functional testing: Use data verification tools for checksums, record counts, and spot-checking. Conduct end-to-end testing of business processes using the migrated data.
- Performance testing: Leverage performance testing tools to simulate user load and measure response times, throughput, and resource utilization.
- Security and compliance testing: Utilize data security scanning tools to detect vulnerabilities and ensure encryption, access controls, and audit trails meet compliance standards.
- Historical data validation: It can be advantageous to also make sure historical data remains accurate and accessible once the migration process is complete—especially with regard to reporting and compliance.
7. Conduct a stakeholder review
- Review meetings: Organize sessions with IT, business stakeholders, and data users to validate the migration outcomes against objectives. Push for formal acceptance testing conducted by business users to ensure the migrated data will function and support business needs as expected.
8. Perform finalization and cleanup
- Data reconciliation: Ensure any data changes in the source system during migration are captured and migrated to the target system.
- Preservation: Archiving or securely disposing of old data and decommissioning legacy systems can help address post-migration data hygiene and security considerations.
9. Prioritize documentation and reporting
- Technical documentation: Detail the technical aspects of the migration, including data mappings, transformations, and tool configurations. Consider the value of also recording why certain decisions were made in addition to the specifics of what was done, as this can enhance the value of the documentation overall.
- Lessons learned: Document challenges, solutions, and insights gained for continuous improvement in future migrations. If available, incorporate information and insights gained during the process to an organizational knowledge base to benefit future migrations and overall organization efficiency.
10. Establish post-migration support
- Issue tracking system: Establish a mechanism for reporting, tracking, and resolving any post-migration issues.
- Invest in migratory success: Whenever possible, a dedicated support team should be established for a defined period post-migration to assist with user queries and issues as data consumers adapt to the new system.
Manual migration testing in the modern world: An unnecessary evil
Now that we’ve stepped out a fairly comprehensive, phase-based approach to data migration testing, let’s go ahead and poke the elephant in the room: Manual data migration testing.
Like many trends in software development and IT operations, manual data migration testing was once a labor-intensive process. It still is. However, while manual testing may be leveraged in specific or niche situations (e.g., for custom applications or small-scale migrations), it is and should be eschewed for increasingly potent automated processes and tools.
To clarify, handling data migration testing manually in increasingly complex and dynamic IT environments introduces several challenges and problems, primarily due to the scale, complexity, and critical nature of the data involved. The manifesting of issues related to manual manual testing processes typically include the following:
Resource intensiveness: Manual testing, especially for large datasets, is extremely time-consuming. Each test has to be designed, executed, and verified manually, which slows down the migration timeline significantly. This necessitates skilled personnel spending considerable amounts of time on repetitive tasks, as opposed to higher-value, strategic activities.
Human error: No one is perfect. So, naturally, manual data migration testing is prone to errors. Intelligent, experienced, well-meaning personnel handling data verification, validation, and comparison can suffer from oversights, inaccuracies, and inconsistencies. This is especially true when dealing with complex data structures and/or large volumes of data.
Limited scalability: As noted before, the volume of data pumping through the hearts of modern organizations is immense. Manual testing processes don’t scale well, as the time and resources they require increase exponentially.
Difficulties in replicating tests: Replication is a vital aspect of comprehensive data testing. When utilizing a manual process, it becomes difficult to conduct and standardize testing efforts across different datasets or migration projects. This lack of standardization, in turn, can lead to variability in testing quality and outcomes.
Inadequate coverage: Given the noted resource and time constraints, manual testing often results in insufficient coverage of data and scenarios. In these cases, critical data issues might go undetected because not all cases or data variations can be thoroughly tested.
Lack of real-time monitoring: Manual testing processes lack the capability for real-time monitoring and alerting for issues that occur during the migration. Immediate issue detection is critical for avoiding downtime, security issues, and degradation of operational efficiency.
Data validation complexity: Validating the accuracy of complex data transformations, relationships, and integrations manually is challenging. Ensuring data integrity and consistency across different systems without automated tools can be nearly impossible for intricate datasets.
Inconsistent documentation: Manual testing processes often suffer from inadequate documentation of tests and outcomes, which can hinder troubleshooting, compliance auditing, and future migration efforts.
Delays in feedback: Finally, the comparatively slow nature of manual testing leads to delayed feedback to the development or migration teams. This can extend the duration of migration projects as issues may only be identified late in the process, necessitating rework.
Amplifying data migration testing at scale
Data contracts can play a significant role in mitigating or minimizing the potential downsides and complications associated with automated data migration testing using ETL (Extract, Transform, Load) tools. Here's how data contracts can address some of the challenges:
Standardization and compatibility
Data contracts define the structure, format, and other specifications of the data to be migrated, ensuring that all parties involved in the migration process have a clear understanding of the data requirements. This standardization can help prevent data mismatches and compatibility issues between legacy and new systems, which are common challenges in data migration.
Data quality and preparation
By specifying the quality and format of data required for migration, data contracts can help ensure that data is cleaned and prepared before the migration process begins. This proactive approach to data quality can reduce the need for manual intervention to rectify data quality issues, which is a significant advantage when using automated ETL tools.
Efficiency and speed
Data contracts can streamline the migration process by clearly defining the data elements and transformations required, allowing for more efficient mapping and processing by ETL tools. This clarity can reduce the time and effort required for initial setup and customization of ETL tools, addressing one of the key considerations when relying on automation for data migration testing.
Error checking and debugging
With clear data contracts in place, automated ETL tools can more effectively perform error checking and debugging. The specifications outlined in data contracts can serve as a benchmark for validating the migrated data, making it easier to identify and resolve issues quickly. This can enhance the reliability of the migration process and reduce the risk of data loss or corruption.
Cost considerations
While the cost of licensing advanced ETL tools and the need for skilled personnel to operate them can be prohibitive, data contracts can help optimize the use of these tools. By clearly defining the scope and requirements of the migration, data contracts can help ensure that resources are used efficiently, potentially reducing the overall cost of the migration project.
Balancing automation with manual oversight
Data contracts support a hybrid approach to data migration testing by delineating areas where automation is most beneficial and where manual oversight is necessary. For instance, they can specify scenarios that require human judgment or domain expertise, ensuring that the strengths of both manual and automated testing are leveraged effectively.
In short, data contracts serve as a foundational element in the data migration process, providing clear guidelines and specifications that can address many of the challenges associated with using automated ETL tools. By facilitating standardization, improving data quality, enhancing efficiency, and supporting error resolution, data contracts can significantly mitigate the potential downsides of relying solely on automation for data migration testing.
Automated data migration testing is ideal, but with a data contract? It can be exceptional.
As the challenges of Big Data continue to compound, automation alone may not be enough for some organizations. That’s why adopting data contracts is a smart way to ensure automated data migration testing can outpace demand.
Data contracts, well-drafted and enforced, are the optimal means to maintain this pace—defining the structure, format, and other specifications of the data to be migrated, ensuring that all parties involved in the migration process have a clear understanding of the data requirements.
This last fact may be most important of all for data leaders seeking to harness the full potential of their organization’s data migration testing processes. That’s why we invite such leaders to join our product waitlist at Gable.ai. There they will find a community of forward-thinking professionals and a platform poised to revolutionize the way we all approach data collaboration.
