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Start with a data contract, not a list of generic tests
A check is useful only when it tests an expectation someone actually has for the data. Before choosing SQL constraints or a testing framework, write down what each important table represents, who owns it, how it is used downstream, and what makes a row valid.
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Record the expectations that can be tested
For each table, identify:
- Row identity: Which field or combination of fields identifies a record?
- Required fields: Which columns may never be NULL, and which may be missing by design?
- Valid values and ranges: Which values are allowed for categories, statuses, quantities, dates, or other fields?
- Relationships: Which values must correspond to a record in another table?
- Expected structure: Which columns must exist for downstream models or applications to work?
- Delivery cadence: How often should the data arrive or be updated?
- Downstream impact: Which reports, applications, or decisions depend on the table?
Turn each expectation into a condition that can pass or fail. “Customer IDs should be present” is a useful starting point; a testable version is “every order must have a non-NULL customer_id.” “The table should be current” needs an agreed delivery cadence and a maximum acceptable delay before it can become a meaningful freshness check.
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Do not start with arbitrary thresholds such as a universal maximum null rate or row-count change. A reasonable limit depends on the source’s delivery agreement and observed operating history. Record who owns each rule and what action a failure should trigger; otherwise a failing test can become a recurring alert with no clear decision-maker.
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Make the contract specific enough that two people would reach the same conclusion about a failure. For example, “quantity should be reasonable” is not yet a check; a stated lower bound such as nonnegative quantity is testable if that matches the business rule. Likewise, “a customer reference should work” needs a clearly identified target key. A useful contract does not need to encode every possible business judgment, but it should distinguish allowed exceptions from accidental gaps. That clarity helps prevent teams from either blocking valid data or quietly accepting values no one intended.
Use database constraints for rules that must hold when data is written
Database constraints are suited to row-level invariants that should be rejected when a write attempts to violate them. They can protect data regardless of which application or process submits it, but support and enforcement differ between database platforms. A declaration in a schema is not, by itself, proof that a particular engine enforces it.
Match common constraints to their purpose
- NOT NULL: Require a value in a column that is mandatory.
- PRIMARY KEY: Declare the table’s identifier, typically combining uniqueness and non-nullness.
- UNIQUE: Prevent duplicate values in a field or a declared set of fields, subject to the platform’s behavior and syntax.
- FOREIGN KEY: Require a reference to correspond to a key in another table, where supported and enforced.
- CHECK: Restrict a row to values that satisfy a condition, such as a nonnegative quantity.
For example, a database may support a row-level rule conceptually equivalent to CHECK (quantity >= 0). Exact syntax, NULL behavior, deferrability, and enforcement options vary. The dbt constraints documentation notes that enforcement depends on the platform and identifies PostgreSQL as supporting standard constraints and row-level CHECK constraints. Verify the behavior in the documentation for your actual database before relying on a constraint to protect production writes.
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Check more than whether a database accepts a constraint declaration. Confirm what happens when a row violates the rule, whether the constraint applies to the writes and tables you care about, and how the platform handles composite keys or references. These details affect whether the constraint actually provides the protection you expect. Do not assume a rule declared in a model or schema is enforced at write time across every engine.
Constraints and transformation tests serve different moments in the data lifecycle. A database constraint can reject invalid writes at the point they reach a table. A test in a modeling workflow can inspect output after loading or transformation and report whether it meets expectations. Neither universally replaces the other: the database may not enforce every declared rule, while a test that runs later does not prevent the invalid row from having already been written.
Add automated tests for models and transformed data
When data is built through transformation models, automated tests can check the result repeatedly as part of that workflow. dbt documents four built-in generic data tests: unique, not_null, accepted_values, and relationships. Its guidance recommends testing every model’s primary key for uniqueness and non-nullness.
Test the primary key and required values
A primary-key test should address both uniqueness and presence. A column can contain no duplicates yet still have NULLs, or contain no NULLs yet still have duplicates. Apply both checks when both properties are part of the contract.
In dbt, an illustrative configuration for an orders model can look like this:
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models:n - name: ordersn columns:n - name: order_idn data_tests:n - uniquen - not_nulln - name: statusn data_tests:n - accepted_values:n arguments:n values: ['pending', 'paid', 'cancelled']n - name: customer_idn data_tests:n - relationships:n arguments:n to: ref('customers')n field: idThis is an illustrative dbt-style YAML example, not a guarantee of compatibility with every installed dbt version. Check the syntax for the version used by your project. The example assumes that the listed statuses are the intended contract; replace them with the values actually allowed by the system that owns the data.
Keep the test attached to the model or column whose expectation it describes so maintainers can see why it exists. A test suite that runs reliably is more useful than a large collection of rules that no one can trace to an owner or contract. In particular, name the tested key and approved domain in a way that makes it straightforward to update both the expectation and its validation when the upstream system changes.
Test accepted values against an owned domain
An accepted-values test is useful for a field whose valid domain is known, such as a status or category. Its list should come from the data contract or owning system, not merely from the values currently present in the table. Otherwise, a newly introduced but legitimate value may cause a failure—or an invalid value already in circulation may be mistakenly treated as acceptable.
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A relationship test checks whether values in one model correspond to values in another. For example, an order’s customer_id can be checked against the customer model’s id. In dbt’s documented behavior, relationship tests automatically exclude NULL values. That means a relationship test alone does not establish that every order has a customer ID. If NULL is invalid, add a separate not_null test; if it is allowed, document that expectation rather than treating the omission as accidental.
This separation is important whenever a reference can be optional. A relationship check answers whether a non-NULL value resolves to a target record; a nullability check answers whether a value must be present in the first place. Treating those as separate questions makes the contract clear and avoids a false sense of coverage from a relationship test that skips missing values.
Cover composite identifiers and schema changes
Some records are identified by more than one field. If a record is unique only by the combination (account_id, effective_date), testing either column individually for uniqueness would express the wrong rule. Use a composite uniqueness check that evaluates the pair as a unit, and confirm that the same identity definition is used by downstream consumers.
Great Expectations documents uniqueness validation across sets of columns, which can suit compound identities. The same framework’s schema use case addresses structural expectations such as required columns. The implementation should fit the project’s stack; the important part is making compound identity and required structure explicit rather than assuming single-column tests cover them.
Schema checks are especially useful when a downstream model or application depends on particular columns being present. They can catch structural changes such as a required column disappearing. They do not replace content checks: a table can have the expected columns while containing duplicate identifiers, invalid values, or broken references. Conversely, content tests may not clearly identify a missing field until a later transformation fails. Decide which structural changes should be tolerated and which should block the workflow.
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When identity spans several columns, state the full combination in the data contract rather than describing one field as “the key” for convenience. A composite uniqueness test can catch duplicate combinations even when each individual field repeats legitimately. If a new field is added or a column is renamed, revisit both the schema expectation and the identity rule: a structural change can leave a table queryable while still changing what downstream users believe a row represents.
Choose checks by where they run and what they can see
Think of constraints and automated tests as complementary layers rather than competing products. The right division depends on what the database enforces, where transformations occur, and how quickly a bad record must be rejected.
| Check layer | When it runs | Good fit | Important limitation |
|---|---|---|---|
| Database constraints | At write time, when the database enforces the declared rule | Required fields, keys, references, and row-level conditions that the engine supports | Support and enforcement vary by platform; verify behavior in that database’s documentation |
| Transformation or model tests | When the project runs its test workflow after building or loading models | Uniqueness, non-nullness, accepted values, and modeled relationships | They report on tested data when run; they do not necessarily prevent invalid data from being written earlier |
| Schema and compound-key checks | At the point the chosen validation workflow checks structure or column combinations | Required columns and identity defined by multiple fields | Choose an implementation that fits the project; do not assume another check covers these cases |
The table describes general roles, not a performance guarantee. The cost of checks on large tables depends on the database, test implementation, and data volume. Measure the workload in the environment where it will run, and prioritize checks according to the impact of missed failures and the cost of running them.
Also compare how each layer surfaces a failure. A constraint can reject a write, so the submitting process needs a way to handle that rejection. A model test can report pass or fail after it inspects the produced data, so the workflow needs to route the result to someone who can act on it. Cross-table or structural checks may be easiest to express in the project’s validation framework, but that does not mean they replace constraints on rules the database can reliably enforce. There is no universal winner: select checks based on enforcement, visibility, and the point in the workflow when the result is useful.
Run checks before outputs are treated as ready
Checks should run after the relevant data has been loaded or transformed and before the affected output is published, consumed, or otherwise treated as ready. In a transformation workflow, run tests as part of the same scheduled or deployment process that builds the models, so a successful build is not mistaken for a successfully validated output.
- Identify the output boundary. Decide which table, model, report, or application dataset must be validated before downstream use.
- Build or load the data. Run the process that produces the version of the data the tests are intended to inspect.
- Run the relevant tests. Include the checks for that output in the same workflow, rather than relying on an informal, separate manual step.
- Read and route the result. Make pass/fail results visible to the people who own the data and the downstream output.
- Apply the rule’s failure policy. Block, quarantine, or alert according to the contract and the impact of the failure.
dbt documentation establishes that data tests run and report pass/fail results, but it does not prescribe one universally correct schedule or failure policy. Those decisions belong to the data owner and the teams relying on the output.
The scheduling decision should follow the way the data is produced and consumed, not an arbitrary universal interval. A check that runs before the data exists cannot validate that delivery; a check that runs after consumers have already relied on the output may be too late for a blocking policy. Define the handoff point at which an output becomes trusted, then place the relevant tests before it. If multiple models feed a downstream result, make clear which tested outputs must pass before that result is published.
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Not every failed check has the same consequence. A missing key in a table used to join records may make the output unsafe to publish, while a noncritical anomaly may warrant an alert and investigation. A quarantine policy can keep affected rows separate from trusted output, but it needs a defined owner and a path for reviewing or correcting those rows. For each rule, specify the action before it fails: block publication, isolate affected data, alert an owner, or combine these responses.
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Also distinguish a test failure from a process failure. A test may have run correctly and found invalid data; a workflow may instead fail to load data or fail to execute the test. Route these situations so that a missing result is not treated as a passing result.
Make failure handling actionable: identify who receives the result, what output is affected, and what must happen before that output can be trusted again. The appropriate response is part of the rule, not an afterthought. For a blocking rule, decide how the release or publication is held. For a quarantine rule, decide how the isolated rows are reviewed and corrected. For an alert-only rule, make clear who investigates and whether consumers may continue using the output while the issue is open.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set freshness and volume checks from a real baseline
Freshness and volume checks help catch a source that is late, incomplete, or unexpectedly changed. They are meaningful only when compared with that source’s expected cadence and a business or delivery baseline. There is no universal threshold established for how late a dataset may be or how much its row count may change.
Use a delivery agreement or observed operating history to define an acceptable delay, expected row-count movement, and any important null-rate change. For example, if a feed is contractually expected on a particular cadence, base the freshness rule on that agreement and the consequences of missing it. For volume, consider the table’s normal behavior and known cyclical changes before setting an alert boundary. Treat a sudden deviation as a signal to investigate, not automatically as proof of bad data.
- Record the expected delivery cadence and the owner responsible for investigating a missed delivery.
- Choose thresholds that reflect the source’s contract or operating history, and document why they are appropriate.
- Revisit thresholds when the source, business process, or delivery schedule changes.
- Do not silently loosen a failing threshold without determining whether the data contract has changed or the source has drifted.
Freshness, volume, and null-rate checks are monitoring rules, not universal declarations of correctness. A row-count change may reflect a real change in activity, and an unusual null rate may have a legitimate explanation. Use the alert to ask whether the data still matches its contract, then update the contract and threshold together if the source agreement or business process has actually changed.
Build the first useful check set in a deliberate order
A practical starting set targets the failures most likely to make a table unusable: missing or duplicate identifiers, missing required values, invalid domains, broken relationships, missing structural fields, and unexpected delivery behavior. Add checks against the contract one table at a time instead of copying an indiscriminate suite of generic rules across the database.
- Choose a high-impact table. Start where invalid data would affect an important downstream use, and identify its owner.
- Write down its contract. Define the row identity, required fields, accepted domains, references, expected columns, and cadence.
- Use database constraints where appropriate. Add supported write-time rules, then verify actual enforcement on the target engine.
- Add model tests for the produced data. Test key uniqueness and non-nullness, valid values, and relationships. Add separate nullability tests where required.
- Cover structure and compound identity. Test required columns and multi-column keys when those expectations apply.
- Integrate execution and ownership. Run checks before output publication and assign a response for each failure type.
- Establish operational baselines. Set freshness and volume expectations from the source’s cadence or observed history, then review them when conditions change.
Keep the contract and tests aligned. If the allowed values, key definition, source cadence, or downstream use changes, update the expectation and its check together. A technically passing suite is not evidence of quality if its rules no longer represent what the data is supposed to mean.
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Frequently Asked Questions
What are the first data quality checks I should add to a database?
Start with the rules that determine whether an important table can be safely identified and used: uniqueness and non-nullness for its key, non-null checks for required fields, accepted values for known domains, and relationship checks for required references. Add schema or composite-key checks when required columns or multi-field identity matter. Base freshness and volume checks on the source’s actual cadence or operating history.
Do database constraints replace data tests?
No. Constraints can reject invalid writes when the database supports and enforces the declared rule. Transformation or model tests inspect data in a test workflow and can check modeled relationships, accepted values, or outputs after transformation. Use both where appropriate, and verify actual constraint enforcement for your database platform.
Does a relationship test also check that the reference column is not NULL?
Not necessarily. dbt documents that its relationship tests automatically exclude NULL values. If every reference must be present, add a separate not_null test; if NULL is permitted, record that in the data contract.
How do I test a record identified by multiple columns?
Define the complete combination, such as (account_id, effective_date), as the identity and use a composite uniqueness check over those columns. Testing each column separately would not establish uniqueness of the pair. Great Expectations documents uniqueness validation across sets of columns.
When should database quality checks run?
Run relevant tests after the data has been loaded or transformed and before the output is published or treated as ready for downstream use. In a transformation workflow, include tests in the same scheduled or deployment process that builds the models. Choose a schedule and failure policy that fit the data contract and downstream consequences; there is no single schedule that suits every system.
What freshness or row-count threshold should I use?
There is no universal threshold established for acceptable delay or row-count change. Derive limits from the source’s delivery agreement or observed operating history, including known cyclical changes, and document the reasoning. Treat an unexpected change as a prompt to investigate, not automatic proof of bad data.
Should every failed test block publication?
Choose the response by rule and impact. A failure that makes the output unsafe may block publication; other failures may quarantine affected rows or alert an owner. Define ownership and the response path in advance, and distinguish a test that ran and failed from a workflow that did not produce a test result.
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The Bottom Line
Build data quality checks from a clear table contract: enforce supported write-time invariants in the database, add repeatable model and schema tests for the produced data, and run them before outputs are treated as ready. Verify platform behavior, test nullability separately from relationships, and base failure policies and monitoring thresholds on the data’s real use and delivery agreement.
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