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Hi,
DMS is the right tool for what you're trying to achieve.
I would suggest that you use the DMS Validation Tasks to make surface potential issues after the migration of your data: https://aws.amazon.com/blogs/database/optimize-data-validation-using-aws-dms-validation-only-tasks/
Server. AWS DMS provides the capability to validate your data as you migrate from
various supported sources to AWS. Data integrity and accuracy is one of key requirements
we often hear about from our customers that determines a successful migration project.
Best,
Didier
Hi Didier, Thank you for your response.
You suggested using the DMS validation service, but I'm unsure how this service can help us with data inconsistencies. Officially, DMS does not transfer foreign keys, stored procedures, triggers, or indexes. We have 100 clients and 785 tables per client. Based on your proposal, would we need to manually check and resolve all discrepancies detected by DMS validation? To be honest, this approach does not seem to align with AWS best practices. If DMS can only migrate data and cannot handle a straightforward task like homogeneous cross-account data migration, it appears to be of limited use. Maybe Is there a talk of combining MySQL dump with DMS? For example, initially copy all the data using MySQL dump and then use DMS for ongoing replication to minimize downtime.
Causes of Inconsistencies
1-Data Type Mismatches: Differences in handling data types between MySQL and Aurora MySQL can cause problems.
2-Triggers and Stored Procedures: AWS DMS does not automatically migrate these, potentially leading to application inconsistencies.
3-Ongoing Changes: High write operations during migration might not be fully captured.
4-Data Transformation Rules: Misconfigured transformation rules could alter data.
5-Character Set and Collation: Differences between source and target databases can lead to data corruption.
6-Network Issues: Intermittent network problems can cause incomplete data transfers.
hope you find my answer helpful.
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