1. First, develop a rollback strategy to revert the environment if critical issues arise during migration. 2. Create an inventory of data, detailing types, volumes, criticality, and current storage locations. 3. Map the data’s current location and target migration destination. Classify it as mission-critical or archival to prioritize effectively. 4. Use secure tools like AWS Data Migration Services or Google Cloud Transfer, or consider data migration specialists for large volumes. 5. Monitor the migration in real time, addressing errors or prolonged processes as they occur. 6. Post-migration, validate the data against the pre-migration inventory.

1. Clear and regular communication with the team. 2. Follow standardized process. 3. Take backup of data. 4. Save both success and failed records with reasons. 5. Proper mapping between the systems.

1. Backup Everything: Create comprehensive backups before initiating any migration process. 2. Test Migrations: Conduct trial runs to identify potential issues early. 3.Monitor Progress:Continuously track the migration process for anomalies. 4. Implement Validation: Verify data integrity post-migration to ensure accuracy.

We at Impetus, have an award-winning, tested & proven accelerator for migration LeapLogic, which has helped 100s of customers achieve fool-proof data migration with over 80% automation. We migrate from almost all legacy and other platforms to target systems of your choice AWS, GCP, Azure, Databricks and Snowflake.

> Step 1: Plan thoroughly 1- Map out the entire migration process, including data sources and destinations. 2- Identify potential risks and prepare solutions in advance. > Step 2: Backup everything 1- Create full backups of all data before migration. 2- Store backups securely in case of issues during the transfer. > Step 3: Test migrations 1- Run smaller test migrations to catch any problems early. 2- Validate that data integrity remains intact after test. > Step 4: Monitor 1- Use monitoring tools to track the migration process. 2- Set alerts for any anomalies or interruptions. > Step 5: Post-migration validation 1- Perform a thorough audit after migration 2- Compare old and new datasets to confirm accuracy.