Workload

Validation & reconciliation

Validation & reconciliation

Validation & reconciliation

Workload scope

Validation and reconciliation covers the evidence needed to prove migrated workloads preserve business meaning before cutover.

Use this hub to find the source-to-target pages that already include workload-specific conversion risks, supported patterns, validation gates, examples, and cutover criteria. The hub is intentionally tied to the migrated child-page corpus so missing child content remains visible during parity review.

  • Databricks to BigQuery - Validate Databricks/Delta→BigQuery with layered gates: golden queries, KPI diffs, pruning verification, MERGE integrity tests, and rollback-ready cutover gates.
  • Databricks to Snowflake - Validate Databricks→Snowflake parity with golden queries, KPI diffs, MERGE integrity tests (reruns + late data), and rollback-ready cutover criteria.
  • Hadoop legacy cluster to BigQuery - Validate Hadoop-to-BigQuery migrations with layered gates: golden queries, KPI diffs, checksum aggregates, pruning checks, and rerun/backfill simulations.
  • Hive to BigQuery - Validate Hive→BigQuery with layered gates: golden queries, KPI diffs, checksum aggregates, pruning checks, and rerun/backfill simulations to prevent drift.
  • Impala to BigQuery - Validate Impala→BigQuery with gates: golden queries, KPI diffs, checksum aggregates, pruning verification, and rerun/backfill simulations to prevent drift.
  • Impala to Snowflake - Validate Impala to Snowflake migrations with layered gates: golden queries, KPI diffs, overwrite/late-window simulations, and cost-aware cutover criteria.
  • Netezza to BigQuery - Validate Netezza to BigQuery migrations with layered gates: golden queries, KPI diffs, pruning checks, and rerun/backfill simulations to prevent drift.
  • Oracle to BigQuery - Validate Oracle-to-BigQuery migrations with layered gates: golden queries, KPI diffs, checksum aggregates, and watermark/restart simulations to prevent drift.
  • Redshift to BigQuery - Validate Redshift→BigQuery migrations with layered gates: golden queries, KPI diffs, checksum aggregates, rerun/backfill simulations, and rollback criteria.
  • Redshift to Snowflake - Validate Redshift to Snowflake migrations with layered gates: golden queries, KPI diffs, checksums, idempotency/restart simulations, and credit-aware criteria.
  • Snowflake to BigQuery - Validate Snowflake to BigQuery migrations with golden queries, KPI diffs, idempotency and late-arrival simulations, checksum aggregates, and cutover criteria.
  • Spark SQL to BigQuery - Validate Spark SQL→BigQuery with layered gates: golden queries, KPI diffs, checksum aggregates, pruning checks, and rerun/backfill simulations to prevent drift.
  • Teradata to BigQuery - Validate Teradata->BigQuery migrations with layered gates: golden queries, KPI diffs, checksums, rerun/backfill simulations, and rollback-ready criteria.
  • Vertica to BigQuery - Validate Vertica→BigQuery with layered gates: golden queries, KPI diffs, checksum aggregates, pruning checks, and rerun/backfill simulations to prevent drift.

Review points

  • Inventory coverage and reconciliation scope
  • Row counts, aggregates, checksums, and profile comparison
  • Golden reports and accepted variance thresholds
  • Exception triage, sign-off, and rollback criteria

Acceptance criteria

A validation & reconciliation page is complete when the migration team can identify source assets, understand conversion assumptions, review unsupported or ambiguous constructs, run validation checks, and decide whether the workload is ready for cutover.

For each related path, confirm that the child page explains what changes, how conversion works, which patterns are supported, what can drift, and which evidence is required before production traffic moves to the target platform.