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To generate synthetic data without broken relationships, model the schema as a dependency graph, create parent keys before child rows, and generate every foreign-key value from the already-created parent key set. Then validate orphan counts, nullability, uniqueness, join coverage, cardinality, and statistical drift as separate checks. If the source database does not declare a relationship, record it as a virtual foreign key and prove that the referenced columns identify exactly one parent row.
What referential integrity means in synthetic data
Referential integrity is an invariant across tables: every non-null value in a child foreign-key column must match an allowed key in its parent table. A generated orders.customer_id therefore has to resolve to one valid customers.customer_id. A value that matches no parent is an orphan row; a value that matches multiple parent rows creates an ambiguous join.
For synthetic data, the practical acceptance condition is zero unintended orphan references after generation and loading. A nullable relationship may intentionally contain NULL, but a non-nullable foreign key cannot be left unresolved. Preserving this invariant matters because broken joins produce incorrect aggregates, misleading test results, failed application workflows, and migration errors even when every individual value looks realistic.
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A dataset can have perfect foreign keys and still be unrealistic. For example, every order may point to a valid customer while order amounts, dates, and customer-order frequency are implausible. Conversely, realistic-looking values can contain orphan references. Test referential constraints first, then measure distributions, correlations, and business rules.
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Build a dependency-aware generation plan
Start by representing each relationship as a directed edge from parent to child. In a typical commerce schema, customers is a parent of orders, and orders is a parent of order_items. Generate independent root tables first, then process children only after their parent key domains exist.
Recommended sequence
- Inventory tables and keys. Record primary keys, candidate keys, nullable columns, composite-key components, and known application-level links.
- Construct the dependency graph. Add an edge for every foreign-key relationship, including relationships absent from the database DDL but enforced by application code.
- Topologically order the graph. Independent roots can be generated in parallel. A child is scheduled only after all required parent key sets are available.
- Generate parent rows and keys. Persist the complete key domain, including any mapping from source identifiers to synthetic identifiers.
- Generate child rows from that domain. Sample or transform only from the parent keys that are valid for the target run. Do not generate an unrelated identifier and hope it collides with a parent.
- Load and validate. Run orphan, uniqueness, nullability, cardinality, and distribution checks before publishing the dataset.
Documentation for Synthesized TDK illustrates this pattern with customers generated before orders, using a foreign_key_generator for orders.customer_id. The important design principle is independent of any particular product: the child generator receives an existing parent-key domain.
One-to-many example
-- Parent keys are created first
CREATE TABLE customers (
customer_id BIGINT PRIMARY KEY,
email VARCHAR(320) NOT NULL
);
-- Child values are selected from customers.customer_id
CREATE TABLE orders (
order_id BIGINT PRIMARY KEY,
customer_id BIGINT NOT NULL,
order_total DECIMAL(12,2) NOT NULL
);If the generator creates 10,000 customers, its order generator should draw customer_id values from those 10,000 generated keys. To model realistic activity, weight the sampling by a customer activity distribution, but never expand the key domain with arbitrary values.
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Composite foreign keys
For a composite relationship, generate and preserve the tuple as a unit. If a child references (tenant_id, account_id), selecting a valid account_id from one tenant and a valid tenant_id from another still creates an orphan or an incorrect match. Store composite keys in canonical column order and validate equality on every component.
Keep joins stable within and across runs
All tables participating in a join must use the same key domain or a deterministic mapping. A separately generated customer identifier in each table is not a shared join key merely because both columns have the same type or format.
Same-run consistency
Snowflake documentation states that join keys can remain consistent between tables generated in one run without a secret when columns are marked as join keys. This is sufficient when a test environment is rebuilt as one coordinated batch. The pipeline should still verify that every participating table uses the same join-key configuration and normalization rules.
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Cross-run consistency
For repeatable values across separate runs, Snowflake documents supplying a consistency secret. Reusing that secret allows consistent values for string join keys across multiple runs. Treat the secret as controlled configuration: changing it intentionally creates a new key mapping, while accidentally rotating it can make otherwise comparable snapshots fail to join.
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For systems without a built-in consistency mechanism, maintain an explicit mapping table from source or logical keys to generated keys. Version that mapping with the dataset release and use the same normalization, case-folding, and encoding rules in every table.
String, numeric, and hashed keys
- Numeric keys: A new sequential range is easy to generate, but it is not cross-run stable unless the assignment is deterministic or stored.
- String keys: Preserve exact collation, case, trimming, and Unicode normalization rules. Two strings that look identical can compare differently under different database collations.
- Hashed keys: Hash the same canonical representation everywhere. Changing delimiters or field order changes the hash and breaks joins.
When the source schema has no foreign-key constraints
Missing DDL constraints do not prove that no relationship exists. Applications often enforce links in code, ETL jobs, naming conventions, or undocumented operational rules. Capture those links as metadata before generation.
Define a virtual foreign key
A virtual foreign key records the child columns, referenced parent columns, nullability, and expected cardinality even though the database does not enforce them. Synthesized documentation describes this approach for application-layer relationships missing from database constraints. MongoDB Relational Migrator also supports synthetic foreign keys for logical relationships absent from source constraints.
The referenced column combination must identify one and only one parent row. Before using a virtual relationship, run a uniqueness check on the proposed parent columns. If duplicates exist, either select a truly unique candidate key or document that the relationship is not a foreign key and define a different matching rule.
Document the metadata
| Metadata | Example | Why it matters |
|---|---|---|
| Child columns | orders.customer_id |
Identifies the values that must resolve. |
| Parent columns | customers.customer_id |
Defines the lookup domain. |
| Null policy | Nullable for guest orders | Distinguishes intentional nulls from failures. |
| Cardinality | Many orders to one customer | Controls sampling and validation. |
| Normalization | Lowercase, trimmed email | Prevents false non-matches. |
| Composite order | tenant_id, account_id |
Ensures tuple matching is deterministic. |
MongoDB Relational Migrator warns that related structures must share values for synthetic foreign keys to populate correctly and notes limitations for some single-field relationships. Confirm the supported relationship shape for the migration path you use rather than assuming every logical link is automatically inferred.
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Handle nulls, empty parents, and cycles explicitly
Nullable foreign keys
Under SQL MATCH SIMPLE semantics, a nullable foreign key can be NULL without matching a parent. Your generator should make the null probability explicit and validate that only nullable columns contain nulls. A non-nullable key must either select an existing parent or fail generation; silently emitting a sentinel such as zero creates an orphan unless zero is a real parent key.
Empty parent tables
If a required parent table is empty, a child generator has no valid key domain. Choose one of three deliberate outcomes: generate the required parent rows, generate zero child rows, or stop the run with a clear dependency error. Do not invent child keys to keep row counts high.
Cyclic relationships
Cycles cannot be solved by a simple topological order. Break the cycle with a nullable or deferred column, generate one side, create the other side, then backfill the deferred values from the now-complete key domain. Validate the cycle before enabling constraints or exposing the data to applications. If neither side can be nullable or deferred, generate a strongly connected component with a coordinated key allocator and commit it atomically.
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Run constraint checks after generation and again after loading into the target database. The second pass catches truncation, type conversion, collation, and bulk-load defects.
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;The expected result is zero unless an exception is explicitly documented. For composite keys, join on every component and apply the null policy consistently.
Parent uniqueness
SELECT customer_id, COUNT(*) AS duplicate_count
FROM customers
GROUP BY customer_id
HAVING COUNT(*) > 1;An empty result is required when customer_id is the referenced key. Run the equivalent query for every candidate key used by a virtual foreign key.
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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;Compare matched_rows with the number of non-null child keys. A high match rate can hide duplicate parents, so coverage must be read alongside the uniqueness result.
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- Count nulls in every foreign-key column and compare them with the declared nullable policy.
- Check that key values conform to type, length, range, collation, and formatting rules.
- For composite keys, verify that no component is null when the relationship requires a complete tuple.
- Check that parent and child row counts fall within the scenario’s planned bounds.
Cardinality and distribution checks
Measure children per parent, orphan-free join coverage, and expected one-to-one or one-to-many ratios. A generator that assigns every order to one highly active customer may pass referential checks while failing workload realism.
Snowflake exposes correlation_coefficient_difference for comparing correlations among non-join columns in source and generated data. Use that metric as a drift signal, not as a substitute for key validation. Investigate large differences in relationships such as customer tenure versus order value or account age versus transaction frequency.
Separate integrity gates from realism gates
| Gate | Core question | Typical failure |
|---|---|---|
| Referential integrity | Does every non-null child key resolve? | Orphan orders. |
| Key uniqueness | Does each referenced key identify one parent? | Ambiguous joins. |
| Nullability | Are nulls allowed exactly where declared? | Unexpected missing relationships. |
| Cardinality | Do relationship counts match the scenario? | Every parent has zero or one child. |
| Distribution | Do values retain plausible ranges and frequencies? | Unrealistic amounts or dates. |
| Correlation | Do non-key variables move together as expected? | Broken behavioral patterns. |
Keep these gates separate in reporting. A run should not be labeled “realistic” merely because its joins work, and a realistic marginal distribution should not excuse failed foreign-key checks.
What common generators can and cannot do
| Approach | Useful capability | Important limitation |
|---|---|---|
| Faker | Open-source Python providers, seeded values, and local fixtures. | Its documentation does not describe schema introspection or automatic foreign-key orchestration. Relational integrity must be designed by the engineer. |
| Synthesized TDK | Schema-aware dependency ordering, foreign-key generation, and virtual foreign-key metadata. | Its “95%+ realistic, statistically similar” and “Configurable (1 to billions)” figures are vendor-stated comparison-table values, not independent benchmarks. |
| Snowflake synthetic generation | Join-key controls, cross-run consistency secrets, and quality metrics such as correlation differences. | Best fit when the generation and validation workflow already runs in Snowflake; configure secrets and join-key metadata deliberately. |
| MongoDB Relational Migrator | Synthetic foreign keys for migration scenarios where logical relationships are absent from source constraints. | Related structures must share compatible values, and supported relationship shapes may impose limitations. |
Faker can support a small fixture by generating a seeded parent list and reusing it in child factories. Pin the Faker version for exact fixtures because provider updates can change seeded results across patch versions. For composite keys, cycles, large dependency graphs, or realistic distributions, use an orchestration layer that understands the schema rather than relying on independent Faker calls.
Troubleshoot broken references
Orphan count is non-zero
- Compare the child key domain with the exact parent key domain produced in the same run.
- Check type conversions, leading zeros, whitespace, case, Unicode normalization, and collation.
- Confirm that the child generator did not use a stale mapping from an earlier dataset version.
- For composite keys, inspect each component and column order.
Join coverage is lower than expected but orphan count is zero
This usually indicates intentional nulls, a wrong join predicate, or a mismatch between the business relationship and the columns selected for the join. Break coverage into null child keys, matched non-null keys, and unmatched non-null keys so the cause is visible.
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Duplicates appear in the parent key
Check whether the supposed key is only a natural identifier with duplicates. Select a stronger candidate key, add the missing tenant or namespace component, or revise the relationship metadata. Do not “fix” duplicates by arbitrarily deleting rows; that can invalidate other relationships.
Reruns no longer join
Verify that the same consistency secret or mapping version is used, that the schema has not changed its normalization rules, and that parent-row selection is deterministic. If repeatability is not a requirement, issue a new dataset version and treat its keys as a new domain.
Release checklist
- Every declared and virtual foreign key is listed with parent columns, null policy, and cardinality.
- Parent tables and candidate keys are generated before dependent rows.
- Composite keys are generated and matched as complete tuples.
- Orphan checks return zero unintended matches.
- Referenced parent keys are unique.
- Null counts comply with schema policy.
- Join coverage and child-per-parent counts meet scenario thresholds.
- Cross-run consistency requirements are documented, including any secret or mapping version.
- Distribution and correlation drift are reported separately from integrity.
- Validation is repeated after loading into the target database.
Frequently Asked Questions
Should validation run on staging files or only after database loading?
Run both. File-level checks catch generator defects before transport; target-database checks catch truncation, implicit casts, collation changes, rejected rows, and bulk-loader behavior that can alter keys.
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Store the mapping or consistency configuration with the dataset release identifier, schema version, and normalization rules. Retain old mappings for consumers that need to compare snapshots, and invalidate them deliberately when the logical key definition changes.
What is the safest response when a proposed virtual key is not unique?
Do not treat it as a foreign key. Add the missing namespace or key component, choose another candidate key, or model the relationship as a non-unique lookup with an explicit disambiguation rule.
Can a realistic row-count target override an empty-parent dependency?
No. A required child row without an allowed parent key violates the relationship contract. Generate the parent, reduce the child count, or fail the run and report the dependency conflict.
Is a vendor percentage for realism an independent benchmark?
No. Figures such as Synthesized’s “95%+ realistic, statistically similar” are vendor-stated values unless an independent study publishes the measurement protocol and results.
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