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The practical approach is a layered quality contract. Start with inexpensive structural checks, then test missingness, uniqueness, accepted values, ranges, distributions, integrity, freshness, and volume. Reconcile independent counts and aggregates between source, staging, and target stages. Finally, add synthetic-data checks for relationship fidelity, sequential logic, fairness, and privacy. Mark every rule as a hard gate, warning, or monitoring metric so failures lead to a defined action.
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What synthetic-data quality checking must prove
Synthetic records are generated rather than copied from an operational source, but they still have to satisfy a contract. That contract describes the dataset grain, columns, types, allowable values, key definition, relationships, batch cadence, and business meaning of important fields.
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Validity: does each row obey the contract?
Validity is structural and rule-based. A valid batch has the required columns, uses the declared types, fills mandatory fields, respects ranges and enumerations, keeps keys unique at the correct grain, and does not create impossible relationships or dates. These checks are suitable for blocking publication when a violation could make downstream calculations wrong.
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Utility: does the dataset preserve what the use case needs?
Utility is about behavior across rows. A generated customer population may have valid identifiers but an unusable age distribution. A synthetic event log may have valid timestamps but impossible event sequences. Compare distributions, correlations, category proportions, temporal patterns, and domain relationships with the intended specification or approved baseline. Utility thresholds are inherently use-case-specific; document the tolerated error rather than applying a universal pass percentage.
Risk: is the valid data safe to use?
Privacy exposure, discriminatory outcomes, and leakage can exist in data that passes every structural check. Include risk tests appropriate to the purpose, access model, and jurisdiction. A synthetic dataset intended for model development needs different safeguards from one used for public demonstration.
Validation layers and what failures mean
| Layer | Example checks | What a failure indicates | Typical action |
|---|---|---|---|
| Schema | Required columns, data types, and contract-required column order | Generator or mapping drift | Fail the batch and inspect the schema version |
| Constraints | Not-null, accepted values, ranges, primary keys, foreign keys, and checks | Values violate the declared contract | Quarantine invalid rows or correct the generator or mapping |
| Missingness | Null rate by column and partition | Incomplete generation, masking, or an unexpected process change | Compare with the specification and approved baseline |
| Uniqueness | Single-column keys, compound keys, and duplicate-entity heuristics | Collisions or duplicated entities | Re-key, deduplicate, or revise generation logic |
| Distribution | Min/max, quantiles, category proportions, and correlations | Synthetic utility drift | Tune the generator and record the tolerated error |
| Integrity | Foreign-key coverage, temporal ordering, and cross-column rules | Broken relationships or impossible states | Repair upstream relationship logic |
| Volume | Total rows, partition counts, and expected ranges | Dropped, duplicated, or overproduced records | Reconcile source, staging, and target counts |
| Reconciliation | Counts, sums, balances, checksums, and source-target joins | Transformation or loading mismatch | Stop downstream publication and investigate |
| Freshness | Last-update timestamp and arrival service-level agreement | Stale or missing batch | Alert and hold dependent outputs |
| Synthetic utility and risk | Similarity to intended distributions, sequence logic, fairness, and privacy exposure | Data may be valid but unsafe or unfit | Add use-case-specific evaluation and governance |
Write the data contract before generating records
A check is actionable only when the expected result is explicit. Record the following in version control alongside the generator:
- Grain: what one row represents, such as one order, account, or event.
- Schema: required columns, types, precision, time zone, and any contract-required order.
- Null policy: which fields may be null and the permitted null-rate range for each partition.
- Allowed values: enumerations, code sets, case rules, and whether unknown values are permitted.
- Numerical bounds: inclusive or exclusive limits, currency, units, and treatment of zero.
- Keys: single-column or compound identity, plus the definition of a duplicate real-world entity.
- Relationships: parent-child cardinality, foreign-key coverage, and cross-column dependencies.
- Cadence: expected batch windows, partition keys, and freshness deadline.
- Quality severity: which rules block publication, which produce warnings, and which are monitored for trend.
Keep business rules readable. “Refund date must be on or after order date” is more useful than an opaque expression that no owner can interpret. Include an owner and a remediation path for every hard gate.
Start with cheap structural and constraint checks
Schema and presence
Check that the expected file or table exists, that every required column is present, and that no type has silently changed. Validate decimal precision, date and timestamp time zones, and encoding before loading expensive distribution tests. If a model contract requires column order, test order as well as membership.
Not-null and accepted values
Run not-null checks on mandatory fields and measure null rates on optional fields by partition. Accepted-value checks should be case-normalized where the domain is case-insensitive and should distinguish a legitimate “unknown” code from a missing value. A sudden rise in nulls can indicate masking failure even when the table loads successfully.
Ranges and cross-column checks
Use inclusive or exclusive bounds deliberately. For example, a percentage may be constrained to 0 through 100, while an account balance may allow negative values. Cross-column checks catch states that individual ranges cannot: a close date cannot precede an open date, a quantity should be positive for a sale, and a cancelled order should have a cancellation timestamp.
Warehouse constraints
dbt documents not_null, unique, primary_key, foreign_key, check, and custom constraints. Support and enforcement vary by warehouse and materialization. Where an enforced constraint fails, dbt reports an error, rolls back the table creation or update, and prevents the invalid operation from completing. Some platforms expose constraints only as informational metadata, so verify actual enforcement instead of assuming declaration equals protection.
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Counts and volume: make row totals a first-class check
Row counts reveal dropped, duplicated, or overproduced records, but the right expectation depends on the batch contract.
Use a range when natural variability is expected
For a generator that intentionally varies population size, set lower and upper bounds and alert on spikes or drops. Store the observed count with the batch identifier so an operator can distinguish a normal seasonal change from a pipeline defect.
Use an exact count for fixed batches
Fixed test fixtures, replay data, and contractual extracts should fail if the count differs from the declared number. An exact check is stronger than a range because even one missing or duplicated row is material.
Use equality for reconciliation
When staging is a transformation of a source or generated landing table, compare counts directly. Equality checks should use the same partition and filter on both sides; otherwise a time-zone or late-arrival difference can create a false failure.
Partition counts to localize failures
Check daily or hourly counts in addition to the total. A total can look normal while one partition is empty and another is duplicated. Partition-level deltas identify the affected window and reduce investigation time.
Test uniqueness at the entity grain
A unique surrogate row identifier does not prove that entities are unique. Two rows can receive different generated IDs while representing the same customer, account, or encounter.
Single-column keys
Check that key values are non-null and occur once. Normalize type before comparison: an integer identifier and its text representation should not be treated as different keys. Decide whether null is forbidden or whether multiple nulls are allowed by the storage system.
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Compound keys
When identity is defined by several fields, test the combination. Examples include account_id plus as_of_date, or order_id plus line_number. A compound-key check catches collisions that individual-column checks miss.
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Duplicate-entity heuristics
For domains without a formal key, normalize case and whitespace and compare a documented set of identifying attributes. Use an acceptable uniqueness proportion when perfect uniqueness is not expected, but explain why duplicates are legitimate. Great Expectations documents both single-column and compound-column uniqueness expectations and notes that a primary key alone may not detect duplicate entities.
Measure missingness, distributions, and synthetic utility
Missingness profiles
Compare null rates by column, partition, and important category. A generator may intentionally reproduce missingness, so the target is not always zero. Define a baseline or specification range and flag changes that would alter model behavior or reporting.
Univariate distributions
Compare minimum, maximum, mean, median, quantiles, standard deviation, and category proportions where they matter. Quantiles reveal tail distortion that a mean can hide. For categorical fields, inspect rare and high-impact categories rather than only the largest values.
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Preserving each column independently is insufficient. Compare relationships that drive the use case: income by region, claim amount by diagnosis, conversion by channel, or event duration by status. Use correlation or association measures as diagnostics, then supplement them with domain rules because a similar correlation can still conceal an impossible subgroup pattern.
Thresholds are use-case-specific
Set tolerances with the data owner. A demonstration dataset may tolerate broad drift, while a model-validation set may require close preservation of tail behavior and interaction effects. Record whether a metric is a hard gate, a warning, or a trend monitor. There is no defensible universal pass-rate percentage for synthetic-data quality.
Integrity checks: relationships, time, and business logic
Foreign-key coverage
Every child key that must resolve to a parent should match an existing parent in the same release or approved reference snapshot. Measure coverage as well as the list of failures. If orphan rows are intentionally allowed, encode that exception rather than silently ignoring it.
Cardinality and cross-table rules
Check one-to-one, one-to-many, and required-child relationships. For example, each invoice may require at least one line, while a customer can have many orders. Validate totals at both table and entity level when child records contribute to a parent balance.
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Check ordering, non-overlapping intervals, and allowed transitions. An event should not occur before its session starts; a shipment should not be delivered before it is dispatched; and a status transition should follow the permitted state machine. A 2024 review by Vallevik and colleagues found that sequential-logic detection was uncommon in existing synthetic-data frameworks, so add these tests explicitly when order matters.
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Reconcile more than row counts
Counts can agree while values are wrong. Reconciliation compares independent measures at each pipeline boundary.
Additive measures and balances
Compare sums of amounts, quantities, fees, credits, and balances after applying identical filters, currency conversions, and rounding rules. Keep a tolerance for documented decimal or exchange-rate effects. A matching total does not prove row-level correctness, so pair aggregate checks with key-level joins or samples.
Category totals and date bounds
Reconcile totals by status, region, product, and other dimensions that should survive transformation. Compare minimum and maximum event dates to detect truncation or accidental future records.
Checksums and fingerprints
For stable extracts, a deterministic checksum over sorted keys and selected fields can detect changes that counts and sums miss. Define field order, null representation, encoding, and sorting so the fingerprint is reproducible across systems.
Cross-table SQL rules
When a rule spans tables or cannot be expressed by a built-in expectation, use custom SQL. Examples include comparing an order total with the sum of its lines, ensuring every ledger balance equals credits minus debits, or verifying that all generated foreign keys resolve in the target table. Great Expectations supports custom expectations for these cases, and its workflow centers on batches, expectation suites, validation definitions, and validation results.
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Validate the most recent source timestamp, ingestion timestamp, and partition arrival against the agreed service-level objective. A technically valid batch that arrives after dependent jobs run is operationally invalid. Alert on stale data, hold downstream publication, and include the late partition and observed timestamp in the incident record.
Implementation patterns with GX, dbt, and SQL
Great Expectations
Great Expectations (GX) organizes checks into batches and expectation suites, then records validation results. Its documentation describes the workflow as: “Validate your Expectations against your data and explore the results.” Use built-in expectations for schema, volume, uniqueness, and common integrity rules. Add custom SQL expectations for cross-table reconciliation and business rules. Store suite versions with the generator version so a changed expectation cannot be mistaken for a changed dataset.
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Use dbt constraints and automated data-quality checks during development, pull requests, and production runs after refreshes. Development catches contract errors before merge; CI protects shared models; production checks detect data-dependent failures. Configure alerts with the failing model, partition, observed value, expected value, and remediation owner. Because warehouse enforcement differs, pair declared constraints with executable tests where necessary.
Custom SQL as the common denominator
SQL is useful when several systems must share the same rule or when a check involves multiple sources. Keep queries deterministic, parameterize the batch or partition, and return both the failure count and representative keys. Avoid hiding logic in a dashboard-only query that cannot block publication or create an audit record.
Place checks through the pipeline
- Landing: verify file or table presence, batch identifier, schema, encoding, and basic row count.
- Generation output: check types, null policy, accepted values, ranges, single-column keys, and compound keys.
- Staging transformations: reconcile partition counts, category totals, sums, date bounds, and transformation-specific rules.
- Relational integrity: test foreign-key coverage, cardinality, temporal ordering, and cross-table business logic.
- Publication: run final volume, freshness, utility, fairness, and privacy gates before exposing the dataset.
- Monitoring: retain metrics and deltas over time so gradual drift is visible even when every batch passes.
Run cheap, high-impact checks first. dbt Labs recommends beginning with uniqueness and non-null checks, then layering accepted values and referential integrity. Expensive distribution and cross-table tests should run after structural failures have been removed, unless the use case makes them a release-critical gate.
Failure handling runbook
- Capture context: record the rule, batch identifier, partition, observed value, expected value, generator and schema versions, and failing keys.
- Classify the cause: decide whether the defect is in generation, transformation, loading, or a legitimate business change.
- Contain impact: quarantine invalid rows or block downstream publication for high-severity failures. Do not let a known-bad batch silently replace a good one.
- Preserve evidence: retain unexpected rows, aggregate deltas, query results, and logs needed to reproduce the failure.
- Fix upstream: correct the generator, mapping, transformation, or contract, then rerun the affected batch and all dependent checks.
- Document exceptions: record the owner, reason, affected scope, compensating control, and expiry date for any approved exception.
Synthetic-data-specific fairness, privacy, and environmental checks
Fairness
Compare representation and outcome-related metrics across protected or otherwise sensitive groups relevant to the use case. A dataset can match overall distributions while underrepresenting a subgroup or encoding unequal relationships. Define which disparities require investigation and which are acceptable for the stated purpose.
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Privacy exposure
Test for memorization, unusually close records, rare combinations, and linkage risk against accessible reference data. Apply access controls, retention limits, and release review even when the generator is marketed as privacy-preserving. Structural validity is not evidence that re-identification risk is low.
Sequential logic
Evaluate event order, transition frequencies, duration distributions, and forbidden paths. Sequence checks are especially important for synthetic clinical, financial, telemetry, and clickstream data used to test stateful systems.
Carbon and operational cost
The 2024 review by Vallevik and colleagues noted that fairness and carbon-footprint dimensions were scarce in existing metric taxonomies. Include generation and validation cost when choosing model size, refresh frequency, and retention, particularly for large repeated batches.
A worked release decision
Assume a synthetic order release contains daily partitions. The contract requires a fixed schema, non-null order and customer keys, unique order-line combinations, valid status codes, order totals equal to line sums within the declared rounding tolerance, all customer keys to resolve, and arrival by 06:00 UTC. Utility monitoring compares category proportions, amount quantiles, and status-transition rates with the approved specification.
- A missing required column is a hard failure: stop immediately and inspect schema drift.
- A 2% null rate in an optional marketing field may be a warning if the contract allows 0–5%.
- An exact daily count mismatch blocks publication when the batch is fixed; a naturally variable feed instead uses a documented range.
- A foreign-key miss quarantines affected orders and triggers investigation of generation or load order.
- A small quantile deviation may warn, while a large shift in high-value orders triggers generator tuning and a utility review.
- A late partition fails freshness even if all rows and totals otherwise reconcile.
This decision pattern makes the response proportional: not every anomaly has the same operational consequence, but every anomaly has an owner and a recorded outcome.
Quick Recap
Release checklist
- Dataset grain and contract version are identified.
- Required columns, types, precision, time zones, and order are validated.
- Null policies, accepted values, ranges, and cross-column rules pass.
- Single-column, compound-key, and duplicate-entity checks match the defined identity.
- Partition and total counts use the correct range, exact, or equality expectation.
- Foreign-key coverage, cardinality, temporal order, and business rules pass.
- Sums, balances, category totals, date bounds, and checksums reconcile where applicable.
- Freshness meets the arrival objective.
- Distribution, relationship, and sequence utility metrics are within documented tolerances.
- Fairness, privacy, and environmental considerations have an owner and a recorded decision.
- Failures, exceptions, evidence, and remediation are retained for audit and reruns.
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