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Automate Data Migration


Migrating data from legacy systems to modern data warehouses? Ensure faster and accurate data migration testing with DataQ’s automated migration testing solution.

Drivers Of Data Migration


Today, most companies have identified the power of modern data warehouses, especially cloud-based databases, and are migrating data from legacy systems to more modern and robust solutions. The process of moving data across databases is called data migration. Some reasons why a company could be migrating data include:

Upgrading computer systems

A company upgrading database software or system hardware will migrate data from older formats to the format matching the latest upgraded version.

Storage capacity expansion

A company expanding storage capacity by adding RAID arrays or similar storage devices will need to migrate data across systems to keep apps live and prevent downtime.

Merging systems (Data Integration)

Companies that have data hosted on disparate databases will look to unify their data storage by migrating data from all standalone sources to a unified data storage system.

Legacy system modernization

A company expanding storage capacity by adding RAID arrays or similar storage devices will need to migrate data across systems to keep apps live and prevent downtime.

Changing computer systems

Changing org-wide tech stack - either system software, architecture hardware, or moving applications to the cloud will require migration of data across legacy and new systems.

Introduction of an additional system

Introducing additional systems for reasons like backup or disaster recovery will require data migration from existing systems to the new ones.

Shift to a centralized database

Merging data from standalone data silos to a centralized data warehouse solution will require data migration from each original source to the final centralized destination.

Moving to the cloud

Migrating data from on-prem solutions to cloud-based solutions like SAP Data Warehouse or IBM data storage systems will require data migration from existing databases to the cloud.

Mergers and acquisitions

Companies merging after an acquisition will need their data stored in a single location, requiring data migration to a unified storage system.

Data Migration Testing


Whatever the reason for data migration, migrating data creates possibilities for data loss, data mismatch, and corruption.

Data migration testing analyzes migrated data and verifies it for structure, format, and authenticity. It ensures that all data has successfully migrated from the legacy system to the new one and that data is in the correct format for the current database solution.

It’s clear why data migration testing is a critical part of the migration process - it ensures data is ready for consumption and that applications can continue to run without interruption or downtime.

Modern data migration tools allow IT admins to test data migration processes before actually migrating all data, either by simulating the migration process or by migrating data and testing it in real-time while still running applications on the legacy system. This allows admins to plan any format changes, tool requirements, or timeline changes that may be needed.

Why Data Migration Testing Is Critical?


Ensures Complete Data Sync

Data between source and destination is compared during data migration testing to ensure that all data has migrated. Migration testing tools highlight any discrepancies in data migration, and the difference can be Synced between source and destination.

Maintains Accurate Data Mapping

Data migration testing verifies that all data is correctly mapped in the migrated database solution as compared to the mapping in the legacy system. Correct mapping of data is critical for uninterrupted application functioning.

Measures Data Integrity

Data Integrity testing post data migration is important to ensure there has been no data loss or corruption during the migration process. Data migration testing tools check data between source and destination to ensure data integrity is intact and there is no loss of data.

Ensures Functionality

The testing tool ensures that all data manipulation and processing functionality works as expected on the migrated system as it did on the legacy system. It also tests for performance - if functionality on the newer system is more efficient.

The 6 key Steps In a Data Migration Stratergy


Define the Migration Process

Defining the migration process is the first step - whether it will be a complete database transfer or partial and progressive, identifying the different tools involved in the migration, determining if a test environment will be used, assigning tasks and resources, and finalizing the migration timeline. This forms the document that is followed throughout the migration.

Assess the Data Source & Prepare It

It’s important to understand the data, data fields, and the legacy system to get an idea of how complex the migration could be. There could be tables that you don’t want to migrate, missing fields that have to be populated from other sources, or data inaccuracies that have to be corrected. Assessing the source will help you clean up and prepare the data for migration.

Data Mapping

Before migration can begin, ETL developers need to map data fields between source and destination correctly. This step involves creating mapping rules to match source fields with the target.

Audit and Maintenance

Once the solution is deployed, it is important to monitor and validate results and performance. This involves auditing data for authenticity and integrity. This step defines the accuracy of the migration.

Execute Data Migration

Extract, transform, and load forms the ETL stage of the migration process and is the actual migration step. This step involves extracting data from the source database, transforming it to match the requirements of the target database, and then loading it into the target database. The ETL stage can be automated with solutions like DataQ.

Testing and Deployment

Testing can be performed in real-time - as data is migrated from source to target, in phases - data is migrated in batches and tested, or post-migration. Data is verified by running unit, system, full-volume, and batch-application tests. Once all tests are complete and all stakeholders are satisfied, the new solution can be deployed, and applications can run on the new solution.

The 3 Different Phases Of Migration Testing


Pre-Migration Testing

Data between source and destination is first tested for migration checks. Pre-migration testing involves defining the scope for migration, mapping fields between source and target, matching the target’s data schema, taking data backups, and creating records for comparison.

Migration Testing

The defined migration processes and systems are checked to ensure all systems are ready for migration to begin. Some activities include checking the migration document and ensuring it is unambiguous and understood by all teams involved, checking elements like ports, hardware, firewall, hosts, etc., to ensure they are configured for the target system, checking connectivity between all components, and performing security checks. This step prepares all systems for migration.

Post-Migration Testing

Migrated data and dependant applications are tested post-migration. Activities include checking whether complete data migration has occurred, ensuring all fields and components have migrated and are correctly mapped, ensuring all schema changes are updated, testing application functionality, and ensuring the application performance is optimal and operations are fully functional on the new database solution.

Challenges That Data Migration Poses


Poor Data Quality

If the data quality is poor in the legacy system (data that is assumed or approximated), you risk migrating this to the new system and compromising functionality. Another risk is the degradation of data in transit during migration because of reasons like the incorrect transformation of data.

Data Volume

Huge data volumes result in long migration times and increase the risk of data corruption and data loss. It also increases the downtime of the system. Automating data migration helps perform it in batches.

Loss of Data

Data loss during migration is a serious challenge. If fields or data are dropped during migration, the record can be void and unusable.

Data Mismatch

There could be a mismatch in data post-migration, either in the data itself (format or data type) or in the data mapping.

Ready To Automate Data Migration Testing


You can increase the accuracy while reducing the execution time of data migration testing by automating processes involved in testing. Businesses can deploy new solutions and do away with legacy systems faster thanks to automated testing, which expedites the process of ensuring data integrity in the migrated solution. DataQ will help you run migration testing processes in parallel and in real-time with just a few API calls or through our easy-to-use GUI. Request a demo to see how exactly it works.