🛠Run pre/post-migration data audits to catch anomalies early. ✅Use checksums and hash comparisons to verify data consistency. 🔁Automate schema validation and record counts between systems. 📊Monitor data flow metrics in real-time during migration. 🧪Use test environments to simulate migration impact safely. 🗂Back up everything before migration—always assume rollback may be needed. 🧠Educate stakeholders on validation roles to avoid last-minute surprises. 🕵️♂️Track all data transformations via lineage tools for transparency.

Apart from mentioned points, understand thoroughly source and target system. Use fuzzy logic/match to ensure data integrity in place.. Additionally perform mock migration to identify proactive problems.

You should begin by analyzing both the legacy and new systems to map the data correctly. Before migration, clean and back up the data to avoid any loss, and during transfer, use validation checks to catch errors early. Post migration, test and verify the data to ensure everything is accurate.

Ensuring data integrity during a system migration starts with meticulous planning. Begin by conducting thorough data profiling to identify inconsistencies or anomalies. Use validation rules and automated ETL pipelines to transform and cleanse data. Implement a phased migration approach, enabling incremental validation of migrated datasets. Run comprehensive test cases to confirm the accuracy of data mappings and relationships. Establish rollback mechanisms in case of failure, safeguarding against data loss. Involve key stakeholders for continuous oversight and transparency throughout the process. By prioritizing accuracy and testing, you can preserve data integrity and achieve a seamless migration.

Run data validation and source to target count checks daily at specified intervals.Maintain a data Mapping document from source to target and ensure that the data migration is happening as per the plan in the document be it in terms of attributes,business rules, transformations,data models,etl pipelines and eventually the data.

IMO, to ensure data integrity: Create a detailed data mapping doc Set up a robust staging environment that mirrors production Run parallel operations with the legacy system during transition Rollback capabilities at each stage Use CDC tools to synchronize ongoing changes Apply data cleansing and transformation rules consistently Compare record counts and data distributions pre/post migration Create point-in-time snapshots for recovery Maintain comprehensive audit logs of all migration activities