SageMaker Lakehouse is built on the Glue Data Catalog and Lake Formation, so a migration barely moves data. It moves enforcement. Here is the failure mode that bites, the cost levers that actually change, and a decision procedure for working out whether the move is worth it at all.
The AWS Glue SAP OData connector demos beautifully and then drifts. A practical look at delta token invalidation, deletes that never delete, partitioning limits on ODP entities, and the licensing line between ODP-RFC and ODP-OData.
AWS Glue zero-ETL replicates seven Zendesk entities, but only three of them ever remove a row. Here is how that gap quietly skews CSAT and knowledge base counts, plus the three IAM layers to wire, the two settings you cannot change after creation, and the CloudWatch metrics that make drift visible before someone spots it in a meeting.
Everyone argues this one on data volume, and volume is the argument that matters least: CRM data is small enough that both architectures handle it comfortably. What actually decides data lake vs data warehouse for CRM analytics is how much point-in-time history you need, how fast the schema churns, what shape your queries are, and who is going to maintain the thing. Includes a decision procedure you can run in an afternoon.