Firstly for any data migration have a impact analysis done, the changes that you are going to make how does those make an impact on a live environment, should be checked on the test data environment to establish that the things will work fine post the migration also. Also validation checks which will be placed, how will those impact on the database and change the outlook of the database. The validation checks should also be checked and tested to make a final conclusion that all the things are working fine.

- Evaluate the current data environment, identify data sources, and understand the structure and relationships of the data. - Cleanse the data before migration to eliminate errors and enhance overall data quality. - Establish clear validation rules that the data must meet during and after the migration process to maintain consistency. - Utilize reliable Extract, Transform, Load (ETL) tools that can help automate the process while ensuring data transformations are accurately applied. - Create comprehensive backups of both source and target data before starting the migration process to ensure that you can restore data in case of failures.

Currently, there are many tools and cloud services that accelerate and assist in data migration while ensuring data integrity, synchronization between the source and destination, end-to-end and in-transit encryption, as well as security. The migration process doesn’t have to be so painful. It’s also important to have clear objectives and to first develop the process in a testing environment with a sample of the data to validate the strategy before applying it to the live data.