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Set up database data quality checks in layers: define what correct data means, enforce write-time rules with database constraints where the engine actually supports them, test transformed tables automatically, and run those checks before data is treated as ready. Then assign an owner and a clear response to each failure. This catches both invalid records and changes that violate the expectations of downstream users.
What a data quality check should do
A useful check turns an expectation about data into a condition that can pass or fail. “The order table should be reliable” is too vague to test. “Every order has an order ID, each ID appears once, status is one of the approved values, and every non-null customer ID matches a customer record” is testable.
Checks matter because an error can travel: an incomplete source record may become a misleading report, an invalid category may disappear from a dashboard filter, and a broken relationship may produce missing or duplicated results in a downstream model. A check does not make data correct by itself. It detects a violation at a point where someone can decide whether to reject, isolate, repair, or report it.
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Keep the checks close to the layer that can best enforce them. A database can reject some invalid writes immediately. A transformation test can validate a model after its SQL runs. A pipeline or monitoring check can flag a late delivery or an unexpected change in row volume. These layers complement one another; no single layer is a complete substitute for the others.
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1. Define a data contract before choosing checks
Start with the tables that matter to a real consumer: a report, application, operational process, or downstream model. For each, record who owns it, who uses it, what identifies a row, which fields are mandatory, what values are allowed, which references must resolve, what shape the table is expected to have, and how often fresh data should arrive.
Write expectations in a way that makes failure unambiguous. For example, “status must be one of pending, paid, or cancelled” is more actionable than “status should be sensible.” “A customer ID may be null only for guest orders” communicates a different rule than “customer ID is required.” If a rule has exceptions, state them rather than allowing a broad test to misclassify valid records.
| Expectation | Question to answer | Possible test condition |
|---|---|---|
| Presence | Which columns must always have a value? | Required field is not null. |
| Identity | What makes a row distinct? | Identifier is unique, alone or in combination with other fields. |
| Allowed values | Which categories or states are valid? | Value belongs to an approved set or satisfies a documented rule. |
| Range or row rule | What numeric or logical conditions must hold? | Quantity is nonnegative, or start date is not after end date. |
| Relationship | Which referenced records must exist? | Each required foreign key resolves to a key in the referenced table. |
| Structure | Which fields and types are part of the expected shape? | Required columns exist with expected definitions. |
| Delivery | How often is this data contractually or operationally expected? | Latest available data is within the agreed delay window. |
Make the contract specific to the dataset. A row-count floor, freshness window, or acceptable null rate has no universal correct value: set it from the source agreement, delivery cadence, and operating history. Record the reason for a threshold and the owner who can review it when the source or use changes.
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2. Enforce write-time rules with database constraints
Use native database constraints for row-level invariants the database can reliably enforce. They are valuable because they can reject invalid writes at the point of entry, before those records reach later models or reports. Common SQL constraint types include NOT NULL, PRIMARY KEY, UNIQUE, FOREIGN KEY, and CHECK.
Choose the constraint that matches the rule
NOT NULLmarks a field that must be populated for every accepted row.PRIMARY KEYidentifies the table’s row key and ordinarily combines uniqueness with non-nullness, subject to the database’s behavior.UNIQUEprevents duplicate values for a key or key combination where the database enforces it.FOREIGN KEYchecks that a reference points to an existing key in another table, where enforcement is active.CHECKexpresses a row-level condition such asquantity >= 0or a permitted range.
For example, a table definition might conceptually include a required order identifier, a required status, and a nonnegative quantity. The exact SQL, supported expressions, treatment of nulls, and enforcement behavior depend on the database engine and version, so treat this as a design sketch rather than portable DDL:
CREATE TABLE orders (
order_id INTEGER PRIMARY KEY,
status VARCHAR(20) NOT NULL,
quantity INTEGER NOT NULL CHECK (quantity >= 0),
customer_id INTEGER
);Check the documentation for the actual database before relying on a declared constraint. Constraint syntax can be accepted without the engine enforcing every rule in the way a team expects. The dbt constraints documentation notes that enforcement varies across platforms; PostgreSQL supports standard constraints and row-level CHECK constraints. That is a platform-specific example, not a guarantee for other engines.
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Constraints are strongest for rules that can be checked against the row being written or a declared key relationship. They are not a replacement for tests of a transformed model, distribution changes, or freshness. Also decide how the application or ingestion process should handle a rejected write: an error that is ignored or retried indefinitely may hide the underlying quality issue.
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Constraints inspect writes at the database layer. Automated data tests inspect the resulting data at a chosen point in a workflow, which makes them useful for validating transformation outputs and rules that are not enforced natively. In dbt-managed transformations, dbt documents four built-in generic tests: unique, not_null, accepted_values, and relationships. Its guidance recommends testing each model’s primary key for both uniqueness and non-nullness.
A dbt-style model property might look like this. Adapt the syntax to the installed dbt version; the current property reference describes data_tests on columns, tables, and views.
models:
- name: orders
columns:
- name: order_id
data_tests:
- unique
- not_null
- name: status
data_tests:
- accepted_values:
arguments:
values: ['pending', 'paid', 'cancelled']
- name: customer_id
data_tests:
- relationships:
arguments:
to: ref('customers')
field: idThe example separates identity, domain, and referential checks rather than relying on a single broad “valid data” test. The key tests catch missing or repeated order IDs; the accepted-values test catches unexpected status labels; and the relationship test checks customer references against the customers model.
Test nullability separately from relationships
A relationship test may exclude null values rather than treating them as failures. The dbt property reference documents that behavior. Consequently, a passing relationship test does not establish that every foreign-key field is populated. If null customer IDs are invalid for this model, add a separate not_null test. If nulls are allowed for a known case, document that contract and avoid imposing a stricter test than the data use requires.
Use accepted-value tests for controlled domains
Accepted-value checks are appropriate for stable, deliberately bounded categories such as a status field. Agree on the authoritative set with the data owner. When a new value is introduced upstream, update the contract and test deliberately; otherwise, a test can either block a legitimate rollout or silently permit an unintended value. For high-cardinality fields or values that are expected to evolve continuously, a hard-coded list may not be the right test.
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Tests are executable expectations, not proof that every aspect of a dataset is correct. A passing uniqueness check cannot establish that the key identifies the intended real-world entity, and an accepted-value check cannot determine whether a value was assigned correctly upstream. Pair automated checks with ownership and a process for resolving failures.
4. Check table structure and composite identity
Some contracts concern the shape of a table, not just its rows. If a downstream model requires columns such as order_id, status, and created_at, test that the expected columns exist. This catches structural changes such as a renamed or removed field before a consumer encounters a less informative failure. Great Expectations documents schema validation as a use case for checking requirements such as required columns.
Identity can also span multiple columns. Suppose an account can have several effective-dated records, but only one row is valid for a given account and effective date. Testing account_id alone for uniqueness would reject legitimate records, while testing effective_date alone would be too weak. The actual identity is the pair (account_id, effective_date).
Use a composite unique constraint if the database can enforce the contract at write time, and/or a test that checks uniqueness across the full column set in the transformed table. Great Expectations documents uniqueness validation across sets of columns. In either case, make the intended key explicit and test that each key component has the appropriate nullability; uniqueness by itself does not always mean that every component is present.
Schema checks should reflect the consumer contract rather than freeze every implementation detail. A change to an unused column may not threaten a downstream reader, while a type or name change to a required field may. Keep the required shape narrow enough to identify breaking changes and broad enough to tolerate safe additions when the contract allows them.
5. Choose where each check belongs
Database constraints and transformation-layer tests overlap, but they differ in timing and scope. Use both when the risk justifies it rather than forcing every rule into one mechanism.
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| Dimension | Database constraint | Automated model or data test |
|---|---|---|
| When it runs | At write time when the engine enforces the declared rule. | In the configured build or test workflow, commonly after the model is built. |
| Typical fit | Required fields, unique keys, references, and row-level conditions. | Transformed outputs, accepted values, model relationships, schema expectations, and compound uniqueness. |
| Platform dependence | Support and enforcement vary by database and constraint type. | Depends on framework, configuration, version, and test implementation. |
| Failure point | The write can be rejected; the writer must handle the database error. | The test reports pass or fail; the workflow owner decides whether downstream work proceeds. |
| Large-table considerations | Enforcement may add work to writes and may require database-specific operational planning. | Tests may scan substantial data; query scope, timing, and compute cost require planning. |
Some rules are best enforced as early as possible, especially invariants whose violation should never be stored. Other rules make more sense after transformation, because they concern the shape or contents of a modeled output. A consistency check across several tables, a data freshness condition, or a volume anomaly generally needs a workflow or monitoring test rather than a simple row-level database constraint.
The available documentation does not establish a universal performance cost or one correct tool placement for all workloads. Assess the effect on write paths and test runs using the actual database, table sizes, query patterns, and delivery schedule. Avoid running an expensive full-table check so frequently that its cost becomes a reason to disable it; consider whether the check can be scoped without weakening the contract.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Run checks before consumers rely on the output
Put tests in the same scheduled or deployment process that builds the data they validate. A practical sequence is to load or transform the data, run applicable tests, and only then mark the affected output ready for consumption. This puts the decision point between a newly built result and a downstream reader that might otherwise assume it is sound.
- Build or load the target data. Identify exactly which tables or models have changed in the run.
- Run checks on those outputs. Include key, nullability, allowed-value, relationship, and schema tests required by their contracts.
- Capture results with the run. Make it possible to identify the failing test, affected table or model, and execution time.
- Apply the rule’s failure policy. Block publication, quarantine affected records, or alert an owner according to the risk and contract.
- Release or recover deliberately. After correction or an approved exception, rerun the relevant checks and make the new result available through the normal process.
For a dbt workflow, include the relevant test command in the same scheduled or deployment process that builds the models. dbt’s documentation describes tests as producing pass/fail results, but it does not define one correct schedule or failure response for every organization. Choose those operational decisions explicitly instead of assuming that installing a test framework makes failed data unavailable automatically.
Set failure behavior by consequence
A duplicate primary key in a table feeding payments or account balances may justify blocking publication. A small, explainable delay in a noncritical reference table may call for an alert while another workflow continues. A malformed subset of source rows might be quarantined if the remaining rows can safely be consumed and the exclusion is visible. These are policy choices: state who decides, who is notified, and what consumers see while an issue is unresolved.
- Block: Do not publish or use the affected output until the failure is fixed or explicitly waived.
- Quarantine: Separate failing rows from usable rows only when partial use is safe and the rejected records remain traceable.
- Alert: Notify a named owner when the issue warrants investigation but should not stop the full workflow.
For each critical check, define the responsible owner and the evidence needed to close an incident. That could include the failing rows, the last known-good run, the source change, and whether consumers used the affected output. Do not treat a waived failure as a permanent fix: record the reason and expiry or review point so a temporary exception does not become an undocumented rule.
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7. Monitor freshness and volume against a baseline
Freshness and volume checks detect changes that row-level constraints may miss. A table can satisfy every key and value rule yet still be stale because its source did not arrive, or contain far fewer rows than expected because an upstream filter changed. These checks only become meaningful when compared with a baseline.
Define the acceptable delay from the source’s delivery agreement or observed cadence. A dataset expected daily and one expected monthly should not share an arbitrary freshness window. Similarly, establish expected row-count movement and null-rate behavior from known operating history or a documented business event. Investigate sudden deviations rather than choosing generic thresholds that have no relationship to the source.
Account for legitimate variation such as weekends, seasonality, backfills, late-arriving records, or an announced source migration. If a threshold is too tight, it creates noisy alerts that people learn to ignore; if too loose, it may detect a problem only after consumers are affected. Revisit the baseline when the contract or delivery pattern changes, and distinguish a real data incident from a planned change.
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8. Roll out checks without creating brittle gates
Adding checks to an existing database can reveal old violations, not just new ones. Before enforcing a constraint or blocking a pipeline, inspect current data and decide how existing failures will be handled. A uniqueness rule cannot be safely applied if duplicate identifiers already exist without a remediation plan. A strict accepted-values list may also uncover legitimate historical labels that were omitted from the initial contract.
- Start with high-impact tables. Prioritize outputs with identifiable consumers and meaningful consequences if data is wrong.
- Profile and review current contents. Find existing nulls, duplicates, invalid values, unresolved references, and structural differences.
- Agree on intended exceptions. Ask the owner whether an observed pattern violates the contract or is valid behavior that needs documenting.
- Introduce tests and observe results. Resolve unclear expectations and false alarms before making a test a release gate.
- Enable enforcement and blocking where appropriate. Plan remediation for legacy rows and a route to recover from a failed run.
- Maintain the contract. Review checks when schemas, sources, business rules, or consumer needs change.
This staged approach reduces the chance that an unreviewed assumption interrupts a production workflow. It does not mean weak checks should remain permanently non-blocking: once the rule is agreed and its failure is consequential, make the gate match that risk.
9. Troubleshoot a failed check
When a test fails, first determine whether the data is wrong, the contract is stale, or the test does not represent the intended rule. Treating every failure as a bad source row can conceal a valid schema change; treating every failure as a harmless test issue can release bad data.
- Not-null failure: Inspect affected records and the source or transformation path. Confirm whether null is forbidden in every case; if so, repair the data path rather than merely removing the test.
- Uniqueness failure: Identify duplicate keys and determine whether they are repeated ingestion, a join multiplication, or evidence that the chosen key is incomplete. If identity is composite, test the full set of key fields.
- Accepted-value failure: Check whether the new value is an upstream change, a typo, or a valid addition. Update the approved domain only after the data owner confirms it.
- Relationship failure: Check for missing parent records, load-order issues, mismatched key definitions, or records that should be exempt. Separately test nullability when null references are disallowed.
- Schema failure: Determine whether a required field was removed, renamed, or changed incompatibly. If the change is intentional, update dependent models and contract together rather than suppressing the check without review.
- Freshness or volume alert: Compare the run with the expected source cadence and recent history, then check for late delivery, partial loading, backfill, or an expected business-cycle shift.
Preserve enough run context to reproduce the failure and trace it to a source, transformation, or schema change. If the output was blocked, communicate its status to consumers; if it was published under an approved exception, record which check was waived and why.
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For a table with known downstream consumers, a sound starting point is a documented owner and contract, a clearly defined row key, required-field checks, domain and relationship checks where applicable, schema checks for consumer-critical structure, and a freshness or volume expectation grounded in the source cadence. Enforce appropriate write-time invariants in the database after verifying actual platform behavior, and test transformed outputs in the workflow that builds them.
Then decide before the first incident what happens when each important check fails. A check without a visible result, owner, and response path is easy to ignore. A layered setup makes it possible to catch invalid writes early, detect transformation regressions, and surface delivery changes before downstream users mistake an unchecked result for a trustworthy one.
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