metadata_change_proposal_process_time

DataHub's asynchronous write architecture can hide processing failures. High Kafka consumer lag combined with ingestion warnings/failures indicates metadata events are queued but not successfully persisting to primary or search storage.

DataHub metadata search results and lineage views showing stale information because Elasticsearch indices are not being updated timely, impacting data discovery and incident response.

When upstream data quality issues occur, teams cannot quickly identify downstream impact (which dashboards, ML models, reports are affected) because column-level lineage is incomplete or not queried during incident response.