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.
Hotel source data is mutable in the past, so an append-only pipeline drifts away from the PMS without anyone noticing until month close. A practical guide to room-night grain, bitemporal modeling with Apache Iceberg, PMS ingestion, guest data scope, and file physics at hotel volumes.
Your lake bucket keeps growing even though nobody added data. Here is where the bytes actually sit: snapshots you never expired, orphan files no metadata points at, metadata JSON accumulation, and the object store's own hidden inventory. Plus the safe order to clean all four up.
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.
Getting Tableau or Power BI to return rows from your data lake takes an hour. The problems arrive three weeks later. Here are the four access patterns worth considering, why fine-grained security and cheap dashboards pull against each other, and the cost levers that actually move the invoice.
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.
You re-run the same extract for the same window and get a different set of rows. Nothing errored. You were paginating a result set that kept changing while you read it. Here's how to build a Zoho CRM to S3 pipeline whose runs are repeatable, from closed read windows to Bulk Read and deletions.
Your dashboard shows four percent more opportunities than Salesforce, and the extra rows were all deleted months ago. Incremental sync on SystemModstamp can never see a record that no longer exists. Here's how to build a Salesforce data lake on AWS that stays equal to the source.