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For companies that already centralize marketing, product, and support telemetry in BigQuery, bringing payments into the same warehouse reduces time to analysis: LTV by cohort can be reconciled with actual cash flows, refund patterns can be correlated with release changes, and chargeback risk models can train on fresher data. Because the connectors live inside BigQuery's managed transfer layer, scheduling, monitoring, and error handling follow the same playbook used for other sources. Ingesting PayPal and Stripe data directly allows near-real-time dashboards for authorization rates, declines by reason code, dispute aging, and settlement timing. Because transfers land into BigQuery tables on repeatable schedules, analysts can build dbt models or Dataform workflows that materialize clean layer views: normalized transaction tables, enriched order facts keyed to user and product dimensions, and "gold" tables for finance and RevOps. Operationally, teams should stage the connectors in non- production projects, validate schema evolution behavior (payment providers occasionally change field sets), and set transfer windows that align with downstream job schedules to avoid race conditions.