Adovo

Adobe Experience Cloud

AEP target dataset filter hides a source dataflow

Confirm source dataflow inventory before treating an empty Target dataset filter as missing ingestion.

October 31, 2026

An empty list in a management screen can prompt the wrong operational response. An Experience League practitioner reports that selecting a Target dataset in Adobe Experience Platform's Sources Dataflows list returns no matching flows, although they expect those flows to exist. The thread has no confirmed root cause or workaround. The immediate job is to establish whether ingestion is missing or only the filtered view is incomplete.

Check the inventory without the filter

Adobe's Sources Dataflows guide explicitly lists Target dataset as a supported filter. Clear all filters, search for a known flow, and record its name, source, status, target dataset, sandbox, and last modified time. Reapply only the Target dataset filter. If the flow disappears, preserve screenshots and the exact dataset identifier; if it remains, examine combinations with other filters and search terms.

Prove where data is landing

Inspect the flow's details and recent run history separately from the list view. Compare its configured target dataset with the dataset the filter shows, including sandbox context. Then check the target dataset for a recent batch or records using the access your team already has. A visible flow without recent data is a different problem from a running flow hidden by a UI control.

Escalate with a reproducible comparison

Give support the organization and sandbox, a flow identifier, the target dataset identifier, the unfiltered result, the filtered result, browser details, and the time of the check. Keep an unfiltered inventory or a governed API extract for operational handoffs until the discrepancy is resolved. Do not delete, recreate, or duplicate a flow to repair a list filter.

Adovo’s view

An interface is a lens, not the system of record. When a filter contradicts a known dataflow, check the underlying flow and dataset before changing production ingestion. The faster decision comes from separating visibility from delivery.

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