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.
Most Salesforce AWS integrations don't fail loudly, they drift. This guide covers the four integration paths between Salesforce and AWS, when to use each, the silent failure mode hiding in every one of them, and the reconciliation job you have to build yourself.
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.
In a SaaS analytics product, the Redshift bill tracks how often queries arrive, not how much data they touch. Here is how the meter actually works, why connection pools bill you while nobody is using the product, how to attribute spend to a tenant, and which isolation choices quietly cost more than they save.
Rendering a dashboard inside your app is the easy part. Tenant isolation, session cost and query mode are what break. A practical walkthrough of the four decisions behind embedded analytics on AWS, the API constraints that lock you in, and the errors you will actually see.
Asked how many open opportunities an account had, the assistant said five. It had nineteen. Retrieval returned five chunks and the model counted them. CRM data is structured and RAG is built for text, so the preparation work is mostly deciding which questions are retrieval and which are queries.
You added a column in Redshift, clicked Sync Design, and it reported success. The column still isn't in the dashboard. That's documented behaviour: one unresolved mismatch anywhere blocks new column information everywhere. Here's how to clear them and stop them coming back.