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Run validation when you need evidence that two outputs reconcile. General Validation’s hosted application manages the Pairs, Tests, and Run history; each comparison executes in your customer-owned Azure environment. Choose a source and target covering the same completed load or time window. The checks establish whether that scope meets the configured rules. They do not establish correctness for untested fields, untested rows, or business logic upstream of the reference source. For function semantics, see Supported scope. For setup, see Creating your first test.

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When you build or change a pipeline

After a mapping, join, transformation, or sink change, compare the completed new output against the reference output. Start with COUNT_ROWS, then add SUM and COUNT_DISTINCT on fields affected by the change. Use VALUE on stable join keys to compare individual fields, and OUTER_VALUE when rows missing from either side must be included. Apply Pair filters if the check covers a deliberate subset. A passing result supports the release decision for those configured checks. Widen coverage when a change can affect more datasets or fields than the initial scope.

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When pipelines run in production

Reconcile each completed load after both source and target outputs are ready. Use COUNT_ROWS and meaningful aggregates as recurring checks, then add keyed value comparisons for the fields where a discrepancy matters. Enable bounded failed-row capture for value and set Tests when investigators need examples of mismatches or missing records. The evidence stays in your Azure Storage; an authorized browser retrieves it directly. See Runs, results & evidence. Use your existing orchestrator and the API to trigger checks after loads. The current hosted application does not provide a built-in schedule editor. See Validating on an ongoing basis.

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When the platform or runtime changes

Before an integration runtime, Spark, or Fabric change, retain a representative reference output and a baseline of Tests. Run the same checks against the completed output after the change. Include counts, aggregates, keyed values, and the field types your workloads rely on. Review the selected scope and any tolerated differences. Coverage is limited to the Pairs and rules in the baseline. A hosted application update and an update to your customer execution runtime are separate events. Check Environment readiness and address any required runtime repair before submitting validation.

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When you migrate or cut over

Pair the old pipeline’s output with the migrated output. Start with row counts and aggregate reconciliation, then add VALUE and OUTER_VALUE on keyed Pairs for the fields that must match. Review the delivered outcomes and retained customer evidence before making a cutover decision. Record the Run ID, scope, and results in your organization’s approval process. The full workflow is in Validating your ADF to Fabric migration.