Test a data table by translating its business rules into assertions, then query for rows that violate them. Start with required fields, unique keys, allowed values, valid references, and sensible row-count or measure bounds. Use reusable tests for rules that recur and custom SQL for one-off logic; when a check fails, inspect the offending records before deciding whether the source, transformation, or expectation needs correction.
What data-table testing checks
Data-table testing here means validating the contents and relationships of database tables, files, or dataframes—not testing how a rendered web table sorts, paginates, or works with assistive technology. A passing test should provide evidence for a defined expectation, not a blanket guarantee that a dataset is correct.
Write each expectation from the data contract and business domain. Common starting points include:
- Requiredness: a field that must be populated contains no nulls.
- Uniqueness: a key or combination of columns does not identify multiple records.
- Allowed values: a categorical field contains only permitted values.
- Referential integrity: each foreign-key value has a corresponding record in the referenced table.
- Bounds and volume: a row count or numeric measure stays within an explicitly justified range.
These checks are examples, not universal rules. For instance, duplicate values may be valid in a non-key field, and an empty table may be legitimate for a particular time period. Make the rule and its exceptions explicit before implementing it.
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Choose a testing approach
Choose based on where the data lives, how the rule is expressed, whether it should be reused, and how failures will be investigated. The documented workflows below do not establish a general ranking by speed, cost, hosting, or licensing.
| Approach | Fits best when | Failure investigation |
|---|---|---|
| SQL or dbt data tests | The table belongs to a dbt project and the rule is naturally expressed as SQL. dbt provides reusable generic tests and custom singular SQL tests. | A test query identifies violating rows; dbt documents storing test failures in a database table for development-time investigation. Check syntax and behavior for your installed dbt version. |
| Great Expectations | You want to define Expectations and validate batches from documented SQL database, filesystem, or dataframe workflows. | Validation results can be used to retrieve unexpected rows for diagnosis. |
| Direct SQL checks | You need a query-driven check in an existing database workflow, particularly for a custom rule or initial investigation. | Return the violating records rather than only a pass/fail count so you can inspect what failed. |
For more on the dbt model, generic, and singular tests, see dbt data tests. Great Expectations documents its expectation concept and validation workflow in Define Expectations, Connect to data, and Run validations.
Build checks that reveal bad records
Start with a specific assertion
Phrase the test as a condition that should hold, then define what a counterexample looks like. For example, if customer_id must be unique, the failure query groups by that field and returns groups with more than one row. If status must belong to a known set, failures are rows whose value is outside that set, with null handling decided separately.
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dbt describes tests as SQL queries that seek records disproving an assertion: a test passes when it returns no failing rows. This pattern is useful beyond dbt because it makes the result actionable: an empty result means the assertion held for the checked data, while returned rows show where it did not.
Use reusable rules for recurring checks
In dbt, generic tests express reusable rules that can be applied to multiple resources with small variations. They suit common properties such as non-null and unique values, relationships, and accepted values. A singular test is a custom SQL query for a rule that is specific to one case. dbt data tests can be associated with models and other resources, including sources, seeds, and snapshots. See dbt’s data-test documentation for version-specific configuration.
Decide what evidence to retain
A pass/fail result is useful for a pipeline gate; the failing rows are useful for diagnosis. During development, dbt documents an option to store test failures in a database table. Great Expectations documents retrieving unexpected rows from validation results. Choose a retention approach that suits the sensitivity and access controls of the data, and avoid retaining unnecessary personal or confidential fields.
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Test relationships across tables
A relationship check asks whether values in one table correspond to valid records in another. Before writing it, establish the intended key, whether nulls are allowed, and how to treat records that are legitimately outside the reference table.
Great Expectations describes three ways to check cross-table integrity:
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- Write a custom SQL expectation: query multiple tables directly when the rule is more naturally expressed as custom SQL.
- Compare multiple data sources: use a multi-source expectation when the relevant query results come from separate sources.
Choose according to where the data resides and how complex the rule is. The Great Expectations guide to validating multiple data sources describes these patterns; its URL is for legacy v0.18 documentation, so confirm current behavior in documentation matching your installed version.
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Put the checks into a repeatable workflow
- Write down the contract. State which columns are required, which keys are unique, which values are permitted, and what relationships or bounds are meaningful.
- Choose the execution point. Decide whether the check belongs in local development, a scheduled pipeline, or CI, based on when the data becomes available and when a failure is useful.
- Implement assertions close to the data workflow. Use dbt tests for dbt resources and SQL-shaped rules; use Great Expectations when its documented source and batch validation workflow fits the data.
- Make failures inspectable. Ensure the query or validation result can identify violating records, and determine whether to retain them for development investigation.
- Review each failure against the domain rule. Decide whether the source data is wrong, a transformation introduced the problem, or the expectation itself incorrectly describes valid data.
- Keep checks maintainable. Reuse a generic rule when it applies consistently; prefer a focused singular test or custom expectation when the business logic is genuinely specific.
Diagnose common failures
| Symptom | Likely cause | Next step |
|---|---|---|
| A test returns rows | The data violates the assertion, or the assertion encodes the wrong business rule. | Inspect the returned records and verify the rule with the data owner before changing data or test logic. |
| A relationship test finds unmatched keys | There may be orphaned records, a key-format mismatch, delayed reference data, or a legitimate exception not represented in the rule. | Compare the unmatched values and key definitions in both tables; settle the exception policy before adjusting the check. |
| A dbt test configuration does not behave as expected | Documentation or syntax may differ from the installed dbt version. | Check the data-test documentation for that version and verify the resource association and test configuration. |
| A Great Expectations validation cannot use the intended data | The connection, source type, or batch retrieval path may not match the documented workflow used by the project. | Confirm the connection and batch selection for the actual SQL database, filesystem, or dataframe source using the relevant version’s documentation. |
| Only a pass/fail signal is available | The workflow may not be retaining or exposing unexpected records. | Use a query that returns failing rows or configure a supported development-time failure-retention path. |
Keep data checks distinct from interface tests
A query that proves a column is unique does not prove that a web table’s sorting, filtering, pagination, keyboard behavior, or accessibility works. Those are interface-testing questions and require checks against the rendered application and its requirements. The data-validation workflows described above address content and relationships; they should not be treated as evidence of frontend behavior.
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Does a passing table test prove the entire dataset is correct?
No. It establishes only that the checked data satisfied the stated assertion at the time and scope of the test.
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Should every column be required to be unique?
No. Uniqueness belongs on a key or other field whose data contract requires it; many valid columns contain repeated values.
Can table-content tests verify sorting or accessibility in a web page?
No. Those are rendered-interface concerns and need separate frontend tests.
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