MSSQL -> MSSQL

This guide is a copy/paste-ready starting point for loading data from MSSQL into MSSQL with dpone.

Status: Batch ETL supported

Identity profile mssql_to_mssql_identity_v1 is hermetically contracted. Vendor-live evidence uses a wide typed fixture across schemas (dpone_src → dpone_it) and covers Docker MSSQL strategies full_refresh, incremental_append, incremental_merge, replace, partition_replace, snapshot_diff, scd2, and backfill (inner replace) via tests/integration/mssql/test_mssql_to_mssql_vendor_live_integration.py. Binary columns use hex character BCP. Bare tinyint / legacy datetime tokens remain hermetic-only (shared DDL mapper dialect collisions with MySQL/ClickHouse spellings). See Route live wide certification.

Vendor-live IT (manual)

docker compose -f docker/docker-compose.integration.yml up -d mssql
export DPONE_RUN_INTEGRATION=1 DPONE_RUN_INTEGRATION_LIVE=1
uv run pytest tests/integration/mssql/test_mssql_to_mssql_vendor_live_integration.py -q

When to use this path

Use this path when MSSQL is the system of record or ingestion boundary and MSSQL is the landing, warehouse, event-log, or downstream replication target.

Copy/paste manifest

# yaml-language-server: $schema=../../src/dpone/schema/etl-batch-manifest.schema.json
kind: dpone.batch.v1

defaults:
  name: mssql_to_mssql_example
  source:
    type: mssql
    connection_id: mssql_source
    options:
      batch_size: 50000
      export_format: csv
  sink:
    type: mssql
    connection_id: mssql_target
    table:
      schema: dbo
      name: orders
    strategy:
      mode: incremental_merge
      unique_key: order_id
      merge_policy: delete_insert
      duplicate_policy: fail

quality:
  gates:
    - id: source_target_rows
      type: row_count_reconciliation
      severity: error
      tolerance:
        mode: pct
        value: 0.1

schemas:
  dbo:
    tables:
      - orders

Run it locally:

dpone plan examples/source-sink/mssql-to-mssql.yaml --format md
dpone run examples/source-sink/mssql-to-mssql.yaml

The checked source file is examples/source-sink/mssql-to-mssql.yaml; CI compares its parsed YAML with this block.

If you change the strategy to full_refresh and empty output is invalid, row-count reconciliation is not enough: it can pass a 0 source / 0 target comparison. Add an explicit non-empty target gate:

quality:
  gates:
    - id: target_min_rows
      type: min_rows
      side: target
      threshold: 1
      severity: error

Supported load strategies

Wide vendor-live evidence covers the strategies below (see Status blurb and manual IT command above). Notes describe runtime contracts.

Strategy	Status	Notes
full_refresh	Supported	Uses staging first, then applies the target-specific finalization plan.
incremental_append	Supported	Uses staging first, then applies the target-specific finalization plan.
incremental_merge	Supported	Default merge_policy: delete_insert; shadow_swap is available for DB targets.
replace	Supported	Uses staging first, then applies the target-specific finalization plan.
partition_replace	Supported	Replaces target partitions represented by staging partition.column; see Load strategies for native/fallback paths.
snapshot_diff	Supported	Requires a complete bounded snapshot and unique_key; applies the configured diff/delete policy.
scd2	Supported	Staging-first SCD2 with technical columns; default expire delete policy.
backfill	Supported	Bounded predicate reload; vendor-live certifies inner replace.

See Load strategies for the detailed algorithm for each strategy. MSSQL CDC is a source capability, not a load strategy. It uses typed CDC offsets and advances source state only after sink success; certify the exact route and environment before enabling it.

Runtime algorithm

This sink does not currently implement StagedLoadPort, so this route records governance_finalization=legacy_post_finalize. Blocking gates run only after the sink has mutated or finalized the target. A failure prevents source-state advancement but cannot roll back that target mutation; inspect and repair or deduplicate the target before retrying. See Load governance.

flowchart TD
    A["Resolve manifest and registry entries"] --> B["Create MSSQL source"]
    B --> C["Plan bounded extract"]
    C --> D["Read through BCP queryout or pyodbc streaming cursor"]
    D --> E["Emit ExtractResult with schema and artifact"]
    E --> F["Plan schema evolution"]
    F --> G["Create MSSQL staging or event batch"]
    G --> H["Load through BCP into staging followed by set-based MERGE/INSERT/REPLACE"]
    H --> I["Apply finalization strategy"]
    I --> J["Run quality and reconciliation checks (legacy_post_finalize)"]
    J --> K["Advance state only after success"]

Strategy behavior

• full_refresh: extract the selected source boundary, load into staging, and replace the target according to the target's safe finalization path.
• incremental_append: extract only the incremental boundary and append rows through staging or event production.
• incremental_merge: load into staging, validate duplicates, then use delete_insert by default; shadow_swap is available where table swaps are supported.
• replace: reload a bounded predicate window through staging and then atomically replace the matching target slice.
• snapshot_diff: compare a complete current source snapshot with the target by unique_key, then apply the configured insert, update, and delete policy.
• partition_replace: extract a complete partition slice, load it into staging, and replace only partitions represented by partition.column.

Snapshot reconciliation is separate from the load strategy. Runtime planning reports that capability as reconciliation.mode=snapshot; in the official dpone.batch.v1 authoring schema, enable it with reconciliation: true.

Schema evolution and type mapping

Schema evolution is enabled by default and runs before the staging/final load path:

1. Read source schema from ExtractResult.schema.
2. Introspect the MSSQL target schema.
3. Apply safe additions and widening operations.
4. Fail breaking changes by default.
5. If configured, route incompatible type changes to __dpone__nc__<column>.

Use Schema evolution and Type mapping matrix when adding columns or changing source types.

Self-service golden path

Copy-paste CJM for the checked-in example (wide vendor-live certified route):

dpone doctor --profile local
pip install "dpone[mssql]"
dpone plan examples/source-sink/mssql-to-mssql.yaml --format md
dpone schema type-matrix --source mssql --sink mssql --format md
dpone run examples/source-sink/mssql-to-mssql.yaml

Landing convention (vault/GitOps-oriented): examples/batch/landing_mssql_to_mssql.batch.yaml.

See Route live wide certification for the maintainer vendor-live IT evidence path (SKIP ≠ PASS).

Runbook

1. Start with dpone doctor --profile local and fix missing extras or native clients.
2. Run dpone plan examples/source-sink/mssql-to-mssql.yaml --format md and review source boundary, staging path, schema evolution, state, and quality gates.
3. Run a small bounded window first.
4. Inspect the run artifact under .dpone/runs/mssql_to_mssql.
5. For incremental jobs, verify state before enabling a schedule.
6. For delete-aware jobs, run reconciliation in report-only mode before enabling physical deletes.
7. Promote the manifest through GitOps after the plan and artifact are reviewed.

Cross-links

• Source -> sink matrix
• Load strategies
• Schema evolution
• Type mapping matrix
• Reconciliation and CDC
• Performance guide

Type contracts and physical design

This flow supports the shared dpone type-governance stack:

• Type inference for source metadata, sampled profiling, confidence, and empty string vs NULL behavior.
• Schema contracts for explicit logical column types, enforcement modes, and __dpone__nc__* variant columns.
• Physical design for target-specific DDL such as concrete SQL types, indexes, partitioning, compression, ClickHouse LowCardinality, and BigQuery clustering.

Use dpone schema infer --manifest ... and dpone schema physical-plan --manifest ... before enabling new table DDL in production.