Data migration is transferring data from one application, database, file system, or storage to another. Businesses migrate data during system upgrades, big data analytics, or data centre consolidation. You could migrate data to other physical servers on-site or on the cloud. Unless you have a robust data migration strategy, you can lose or degrade quality during the migration, which makes the process critical.
What are the types of data migration?
This includes moving data from one on-premise database or storage system to another to improve security, compliance, and processing.
This includes migrating data from an on-premise database to cloud storage or a data warehouse. It is becoming common for organisations to choose this option given the security, scalability, and flexibility. You can reduce the cost of storing the data and managing on-site servers. Data on the cloud is well managed and offers deeper insights for business benefits.
This happens when an organisation migrates their application from an on-premise to a cloud server. Migrating an entire application to the cloud is best for higher security, scalability, and ease of usability. It also reduces the cost of maintaining the application.
What are data migration challenges?
If you do not have a data migration strategy, this process can pose several challenges, such as –
- Data loss – It is easy to lose data while migrating from one location to the other. You can prevent this by creating a backup on the original server before initiating migration.
- Data corruption -If you apply the wrong rules or validation to the source or destination, the migration process can corrupt the data. Creating a backup and setting the right validation rules prevents data corruption.
- Data compliance –When migrating to a new location, organisations need to be aware of the new governance legislation. For example, the GDPR framework prohibits the sharing of personal data outside the EU and penalises if you store excessive data when it serves no purpose.
- Data maintenance –After migration, organisations must maintain data and comply with the governance principles.
What are the data migration methods?
- Big bang migration -This strategy aims to transfer all the data within a set time slot. This method entails a lower cost, less complexity, and faster completion. However, you would need to take the system offline for the duration of the big bang migration. There is also a risk of losing the data if you do not back it up.
- Trickle migration -This migration happens in phases where both the systems work together without any downtime. There is less risk of losing data; however, the migration process is complicated and needs better planning.
What are the stages of data migration?
Any data migration typically goes through the following stages –
- Data extraction -Extract the data from the current system before starting the migration process.
- Data transformation -Ensure that the metadata reflects the actual data and matches it to its new form.
Data cleansing -Clean any corrupt data and deduplicate by running tests.
Data validation -Test the data on a mock environment to know if the migration will yield the desired results.
Data transfer -Transfer the data to the new location and check for any errors.