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Example use cases

Data migration validation: A warehouse migration moves data from an on-premise Oracle database to Snowflake. Before cutover, a Comparison metric validates that COUNT(*) and SUM(revenue) match between both systems within 0.01% tolerance — running on every incremental load during the migration window. Any drift above the tolerance pauses the cutover until the discrepancy is resolved. Staging vs. production parity check: Before promoting a dbt model change from staging to production, a Comparison metric verifies that the output of the staging model matches the production model on key aggregates within expected tolerances — catching unintentional logic changes before they affect live dashboards. Pipeline checkpoint reconciliation: A comparison between the raw ingestion layer and the curated mart validates that COUNT(DISTINCT order_id) is consistent at every pipeline stage (Source → Lake → Warehouse → Mart). A drop at any stage surfaces the transformation that dropped records.
Both datasets must be accessible to Prizm through configured source connections. For row/value-level diff mode, ensure the service account has SELECT access to both tables and that a common key column exists for joining.
Use Comparison metrics to validate data migrations continuously — not just at cutover. Configure them to run before, during, and after migration so Prizm tracks parity throughout the process and immediately alerts if it breaks.