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Generate parent keys first, generate child rows only from those keys, and validate every relationship after loading. That sequence prevents orphan records and keeps joins meaningful. If a relationship exists only in application code, model it as a virtual foreign key and verify that the referenced columns identify exactly one parent row. Finally, test relational constraints separately from statistical realism: a dataset can have valid foreign keys yet still have implausible distributions, correlations, or cardinalities.
What referential integrity means in synthetic data
Referential integrity is the invariant that every non-null foreign-key value in a child table resolves to an allowed key in its parent table. In a conventional one-to-many design, each orders.customer_id must match one existing customers.customer_id. A nullable foreign key may intentionally contain NULL; an arbitrary value that matches no parent is an unintended orphan.
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Broken references are more than a constraint error. They make inner joins silently lose rows, cause left joins to produce unexpected nulls, and can distort aggregates such as orders per customer. A synthetic dataset therefore needs both valid key relationships and a documented policy for nulls, empty parent tables, composite keys, and cycles.
The practical acceptance test
- Every non-null child key matches a parent key.
- Every parent key is unique, or the model explicitly permits a non-unique referenced column.
- Composite foreign keys match on all constituent columns.
- Nullable relationships contain
NULLonly where the schema permits it. - Expected join coverage and one-to-many cardinalities remain within the intended range.
Generate tables in dependency order
Build a dependency graph in which an edge points from a parent table to a child table. Topologically sort that graph, create independent root tables first, and generate each dependent table from the key domain already produced. This is the central pattern documented for schema-aware generators: parent records are created before children, and a child foreign-key generator samples or transforms keys from the existing parent set.
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One-to-many workflow
- Inspect the schema. Record primary keys, candidate keys, nullable columns, composite-key columns, and declared foreign keys. Also collect application-level relationships that are absent from DDL.
- Generate root entities. Create customers, accounts, products, or other tables with no upstream dependencies. Materialize their primary or candidate-key values before creating children.
- Freeze each key domain. Expose the generated parent keys as the only allowed source for child foreign keys. Do not independently ask a generic fake-data provider to invent child IDs.
- Generate child rows. For each order, payment, shipment, or event, select a key from the appropriate parent domain. Apply the desired distribution, such as uniform, weighted, or activity-based selection, without introducing values outside the domain.
- Load and validate. Run orphan, uniqueness, nullability, coverage, and cardinality checks before publishing the dataset or enabling constraints.
For a simple customers-to-orders model, the order is therefore customers first, orders second. If the parent table is intentionally empty, a non-null child foreign key cannot be generated legally; either create parents, allow a nullable child key, or generate zero child rows.
Cycles and mutually dependent tables
A cycle cannot be satisfied by a straightforward topological order. Break it deliberately: create one side with a nullable or deferred key, generate the other side, backfill the deferred values from the now-existing key domain, and validate the complete cycle before enforcing non-null or foreign-key constraints. This is safer than inventing temporary IDs that might survive into the published data.
When the source schema has no foreign-key constraints
Absence of a database constraint does not prove that tables are unrelated. Applications often join on columns that were never declared as foreign keys, especially in legacy databases, warehouses, exports, and document-to-relational migrations.
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Define a virtual foreign key
Capture each logical relationship in generation metadata as a virtual foreign key. A virtual relationship tells the generator that a child column or column combination must draw values from a parent key domain even though the source DDL does not enforce it.
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The referenced combination must identify one and only one parent row. If (country_code, customer_number) is used as the logical key, test its uniqueness in the parent before generation. A non-unique reference creates ambiguity: one child value could match several parents, so joins would not be deterministic.
MongoDB Relational Migrator documentation describes synthetic foreign keys for relationships missing from source constraints. Its model requires related structures to share values; if the source rows do not contain a common value, the synthetic relationship cannot populate reliably. Treat this as a migration-specific warning and verify the actual data before enabling the mapping.
Composite and natural keys
For a composite relationship, generate and store the complete tuple together. Sampling country_code and customer_number independently can create combinations that never existed in the parent table. The child generator should select a parent tuple first, then copy all components, or use a deterministic mapping that preserves the tuple as a unit.
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Every table participating in a join must use the same key domain or a deterministic mapping to that domain. Re-generating similar-looking strings independently is not enough: two UUID-like values can be syntactically valid yet fail to join.
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Same-run consistency
Within one generation run, maintain a shared key registry or key-mapping context. Generate a parent key once, pass it to every dependent row, and prevent a second generator from replacing it with a new value. This applies to integer IDs, strings, hashes, and composite keys.
Cross-run consistency
Repeatability across separate runs is a distinct requirement. Snowflake documentation states that join keys are consistent among tables in one run without a secret. For string join keys that must remain consistent across multiple runs, provide and reuse the same consistency secret. Without that secret, do not assume a key generated today will match a key generated in a later run.
Document the scope of repeatability: same run, same seed, same consistency secret, or a persistent mapping table. Rotating a secret intentionally changes the mapping and can break incremental joins.
Nulls, empty parents, and SQL matching behavior
Nullability must be explicit in the generation contract. Under SQL MATCH SIMPLE behavior, a nullable foreign key can become NULL rather than matching a parent. That is valid only when the relationship is optional. A non-nullable foreign key has no legal fallback when its parent key is absent, so generation should fail fast or produce no dependent row.
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Choose a policy for each relationship
- Required relationship: generate a parent first and reject any child row that cannot obtain a parent key.
- Optional relationship: define the null rate and generate either
NULLor a valid parent key; never use a sentinel value unless the schema explicitly defines one. - Empty parent is allowed: produce zero dependent rows or keep the child key null, according to the schema.
- Empty parent is not allowed: treat it as a configuration error and stop the pipeline.
Validation queries and quality gates
Run relational checks after generation and after loading into the target database. The following SQL catches the most common failures for an orders-to-customers relationship:
-- orphan check
SELECT COUNT(*) AS orphan_count
FROM orders o
LEFT JOIN customers c ON c.customer_id = o.customer_id
WHERE o.customer_id IS NOT NULL AND c.customer_id IS NULL;
-- parent uniqueness
SELECT customer_id, COUNT(*)
FROM customers
GROUP BY customer_id
HAVING COUNT(*) > 1;
-- join coverage
SELECT COUNT(*) AS child_rows,
COUNT(c.customer_id) AS matched_rows
FROM orders o
LEFT JOIN customers c ON c.customer_id = o.customer_id;The orphan query should return zero unintended orphans. The uniqueness query should return no rows when customer_id is the referenced key. Join coverage lets you calculate the matched fraction as matched_rows / child_rows; interpret unmatched nullable rows separately from invalid non-null values.
Additional checks
- Nullability: count nulls in required foreign-key columns and compare optional columns with their configured null rate.
- Composite matching: join on every key component and test for unmatched non-null tuples.
- Cardinality: compare children per parent with the intended one-to-many or one-to-one pattern.
- Row counts: verify that parent and child volumes meet the requested scale and that filtering did not remove dependencies.
- Uniqueness: test primary keys and every candidate key used as a virtual reference.
- Join coverage: measure matched, unmatched, and intentionally null child rows independently.
Validate realism separately from constraints
Referential integrity answers whether rows can join. It does not answer whether the data looks like the source. Run a second quality track for distributions, correlations, frequencies, and scale.
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Compare numeric and categorical distributions, date ranges, missingness, and relationship cardinalities. Snowflake exposes a correlation_coefficient_difference metric for comparing correlations among non-join columns between source and generated data. A large difference can reveal that independently generated columns have lost dependencies even though every foreign key is valid.
Keep the two verdicts separate: “all required references resolve” and “statistical drift is within tolerance.” A dataset can pass one and fail the other.
Tool and implementation choices
| Approach | Strengths | Limitations and cautions | Best fit |
|---|---|---|---|
| Hand-built SQL or Python pipeline | Full control over key domains, ordering, null policy, and validation queries. | Engineers must model dependencies, composite keys, cycles, and distribution controls themselves. | Small schemas, custom test fixtures, or teams needing transparent code. |
| Faker | Open-source Python providers, fake values, and seeding for local fixtures. | Its documentation does not describe schema introspection or automatic foreign-key orchestration. Relational integrity must be designed by the engineer. Provider updates can change seeded results across patch versions. | Deterministic unit and integration fixtures when you pin the package version and manage key sharing explicitly. |
| Synthesized TDK | Schema-aware dependency ordering, foreign-key generation, and virtual foreign-key metadata. Its documentation describes parent-before-child creation and foreign keys that reference existing primary keys. | Its comparison table’s “95%+ realistic, statistically similar” statement is vendor-reported, not an independent benchmark. The same table lists scale as “Configurable (1 to billions),” also a vendor-stated capability. | Relational generation with explicit dependencies, large requested scales, and application-level relationships. |
| Snowflake synthetic data features | Join-key controls, consistency secrets for repeatability across runs, and quality outputs including correlation differences. | Cross-run string consistency requires reusing the same consistency secret; same-run consistency alone does not guarantee future-run matches. | Teams already operating generation and validation workflows in Snowflake. |
| MongoDB Relational Migrator | Synthetic foreign keys for logical relationships absent from source constraints. | Related structures must share values for population to work; verify the relationship data and any documented single-field limitations before migration. | Migration projects translating logical relationships into relational targets. |
Can Faker alone guarantee relational integrity?
No. Faker can create realistic-looking values and repeat them with a seed, but its documented role is value generation rather than schema introspection and dependency orchestration. You can build integrity around it by generating parent keys once, storing them, sampling those keys for children, pinning the Faker version, and running the SQL checks above. For composite keys, cycles, large dependency graphs, or source-like distributions, a relational generator or a more substantial custom pipeline reduces the amount of orchestration you must maintain.
Troubleshooting broken references
Orphan count is non-zero
- Check whether child rows were generated before parent keys were materialized.
- Compare data types, collation, trimming, and case normalization on both sides of the join.
- Confirm that the child generator reads the current parent key domain rather than an old cache.
- For virtual keys, verify every referenced column and the parent’s uniqueness.
- Quarantine or regenerate invalid child rows; do not silently insert placeholder parent records unless the model explicitly requires them.
Join coverage is lower than expected
Separate intentional nulls from invalid non-null keys. Then inspect filters, partition boundaries, and incremental-run mappings. If runs must join to one another, confirm that the same consistency secret or persistent mapping was used. A new secret creates a new string-key mapping by design.
Duplicate parent keys appear
Duplicates usually indicate that a supposed primary or candidate key was generated independently in multiple partitions, or that a composite key was assembled from unconstrained parts. Centralize key allocation, enforce uniqueness before child generation, and regenerate affected partitions.
Relationships are valid but realism is poor
Move to the statistical validation track: compare distributions, missingness, cardinality, and non-join-column correlations. Adjust child-selection weights and conditional generation rather than weakening foreign-key checks.
Quick Recap
Production checklist
- Inventory declared and application-level relationships.
- Represent missing DDL relationships as virtual foreign keys.
- Prove referenced columns are unique, including composite combinations.
- Topologically order tables and define a strategy for cycles.
- Generate and persist parent key domains before children.
- Specify null, empty-parent, and required-versus-optional policies.
- Choose same-run and cross-run consistency requirements; configure a reusable secret when required.
- Run orphan, uniqueness, nullability, composite-match, row-count, and join-coverage checks.
- Run distribution and correlation checks independently of constraint checks.
- Pin fixture-generator versions when exact seeded output matters.
- Fail the pipeline on unintended orphans or duplicate referenced keys, and retain validation results with the dataset release.
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