Data Pipeline and Migration Quality

A successful job proves execution completed; it does not prove the business data is correct. Data quality therefore combines pipeline checks, business reconciliation, exception ownership, restart evidence, and end-to-end use of the resulting data.

Pipeline coverage

flowchart LR
    A[Transactional source] --> B[Event or extract]
    B --> C[Staging]
    C --> D[Transformation]
    D --> E[Data mart]
    E --> F[Dashboard or consumer]
    A -. counts fields totals .-> G[Reconciliation evidence]
    F -. compare outcome .-> G

Validate schema compatibility, completeness, freshness, source-to-target mapping, transformation rules, null/default/duplicate/reject handling, incremental watermarks, retry and idempotency, key integrity, row-level access, reporting totals, and batch windows.

Accounting equation

Input = successfully processed + rejected with reason + intentionally filtered

Support this equation with counts by tenant/date/status/batch, financial control totals, critical field comparisons, hashes for large unchanged partitions, and an exception ledger containing record ID, reason, owner, and disposition.

Migration lifecycle

flowchart LR
    A[Discovery] --> B[Source profiling]
    B --> C[Mapping approval]
    C --> D[Repeated dry runs]
    D --> E[Failure and restart tests]
    E --> F[Dress rehearsal]
    F --> G[Cutover and final delta]
    G --> H[Stabilization]
    H --> I[Business and technical reconciliation]

Each source record must finish in exactly one known state: migrated, rejected with a documented reason, or intentionally excluded with approval. Sampling can supplement this evidence but cannot replace full-population reconciliation for record accounting.

Practical cutover checklist

• Establish an authoritative source snapshot or cutoff timestamp.
• Give each batch and record a traceable migration ID.
• Validate counts, critical keys and fields, relationships, attachments, and control totals.
• Normalize approved transformations before hash comparison.
• Test interruption, checkpoint, restart, rerun, and idempotency.
• Validate the final CDC/delta after bulk load.
• Run critical business journeys using migrated data.
• Rehearse rollback and measure the complete cutover window.

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