Postgres -> BigQuery

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

Status: Batch ETL supported

CSV wire + postgres_to_bigquery_analytics_v1 type profile are hermetically contracted. Vendor-live evidence uses a wide typed fixture and covers Docker Postgres → BigQuery strategies full_refresh, incremental_append, incremental_merge, replace, partition_replace, snapshot_diff, scd2, and backfill via tests/integration/postgres/test_postgres_to_bigquery_vendor_live_integration.py (billing-enabled GCP project required). bytea is exported as RFC 4648 base64 for BigQuery CSV BYTES (native Postgres \x… hex is rejected by load jobs). Incremental deltas targeting BigQuery use COPY file export so that wire encoding applies. See Route live wide certification.

Vendor-live IT (manual)

docker compose -f docker/docker-compose.integration.yml up -d postgres
export DPONE_RUN_INTEGRATION=1 DPONE_RUN_INTEGRATION_LIVE=1
export BIGQUERY_DWH_PROJECT_ID=<gcp-project>
export BIGQUERY_DWH_SERVICE_ACCOUNT_KEY_FILE=/absolute/path/to/sa.json
export DPONE_IT_BQ_DATASET=dpone_it_postgres
uv run pytest tests/integration/postgres/test_postgres_to_bigquery_vendor_live_integration.py -q

Never commit the service-account JSON. Missing credentials or billingNotEnabled are reported as SKIP, not PASS. Live configs use batch_commit_mode=whole (direct CSV load jobs); separate batched mode requires an explicit object_storage bucket.

When to use this path

Use this path when Postgres is the system of record or ingestion boundary and BigQuery 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: postgres_to_bigquery_example
  source:
    type: postgres
    connection_id: postgres_source
    options:
      batch_size: 50000
      export_format: csv
  sink:
    type: bigquery
    connection_id: bigquery_dwh
    table:
      schema: landing
      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:
  public:
    tables:
      - orders

Run it locally:

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

The checked source file is examples/source-sink/postgres-to-bigquery.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 (Docker Postgres + billing-enabled BigQuery) covers every BigQuery factory strategy below. Runtime contract details remain in Load strategies.

Strategy	Status	Notes
full_refresh	Supported	Wide vendor-live PASS; staging then target finalization.
incremental_append	Supported	Wide vendor-live PASS; watermark via incremental_column.
incremental_merge	Supported	Wide vendor-live PASS; default merge_policy: delete_insert.
replace	Supported	Wide vendor-live PASS; bounded predicate window.
partition_replace	Supported	Wide vendor-live PASS; replaces partitions from staging partition.column.
snapshot_diff	Supported	Wide vendor-live PASS; requires complete snapshot + unique_key.
scd2	Supported	Wide vendor-live PASS; __dpone__is_current / __dpone__row_hash via enricher.
backfill	Supported	Wide vendor-live PASS for inner replace window.

See Load strategies for the detailed algorithm for each strategy. Postgres xmin boundaries and CDC are source capabilities, not load strategies. Select them explicitly through supported source configuration; their state advances only after sink success. Certify the exact CDC 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 Postgres source"]
    B --> C["Plan bounded extract"]
    C --> D["Read through server-side cursor or partitioned range scan"]
    D --> E["Emit ExtractResult with schema and artifact"]
    E --> F["Plan schema evolution"]
    F --> G["Create BigQuery staging or event batch"]
    G --> H["Load through load job into staging table followed by set-based finalization"]
    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 BigQuery 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[gcp,postgres]"
dpone plan examples/source-sink/postgres-to-bigquery.yaml --format md
dpone schema type-matrix --source postgres --sink bigquery --format md
dpone run examples/source-sink/postgres-to-bigquery.yaml

Landing convention (vault/GitOps-oriented): examples/batch/landing_postgres_to_bq.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/postgres-to-bigquery.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/postgres_to_bigquery.
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.