We’ve been working with SailPoint IdentityIQ for the last couple of weeks trying to stabilize Azure AD delta aggregation for a tenant with 400K+ targeted user accounts, and I wanted to validate our approach with the broader community.
What we’ve tried so far:
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Schema reduction (removing non‑essential attributes)
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Removing PIM / privileged role aggregation - Though it was important for identity governance - To isolate the issue with PIM this was removed
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Running delta on a single task server
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Running delta on multiple task servers with user partitioning
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Tuning thread counts and connector parameters in application XML
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Validating that partitioning is successfully created and ROProcessors are running
Despite all of the above, delta aggregation does not consistently converge and remains in “DeltaSync in progress” state for extended periods, while full aggregation reliably completes within ~24 hours.
Based on this behavior, the recommendation we’ve received is to:
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Create multiple logical Azure AD applications
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Split the user population (e.g., core users vs privileged users, or segmented filters)
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Use full aggregation for large populations and delta aggregation only for smaller, bounded scopes
My questions to the community:
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For large Azure AD tenants (300K–500K+ users), is this a known and accepted architecture?
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Have others found Azure AD delta aggregation to be unreliable at scale, even with partitioning?
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Is relying on full aggregation (or a split full+delta model) considered best practice in these scenarios?