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Use data cleaning to correct, standardize, or remove known data problems. Use data validation to check whether data meets explicit requirements. Most dependable workflows use both: define what acceptable data means, clean issues according to documented rules, then validate the result—and keep checking new data as it arrives.
The practical choice is not usually one tool or the other. It is deciding where data needs a defensible correction and where it needs a check, and then deciding what should happen when a check fails.
What is the difference between data cleaning and data validation?
Data cleaning changes data; data validation checks it. Cleaning addresses identified problems such as inconsistent formats, duplicates, missing values, or malformed records. Validation tests data against defined expectations and reports whether those checks pass.
The distinction matters because finding a suspicious value does not tell you what the correct value should be. That decision requires knowledge of the data and its intended use. Similarly, a passing validation check only establishes that the checked rules passed; it does not prove every value is true or every defect has been found. Microsoft Learn treats profiling, cleansing, validating, and monitoring as distinct data-quality activities. Microsoft Learn’s data governance guidance recommends consistent data definitions and formats.
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For example, a check can report that a date field contains values in an unexpected format. That is a finding, not a decision about whether to rewrite the values, reject the records, or treat the format as an allowed exception. Cleaning follows only after someone determines the approved representation or handling rule.
Cleaning is corrective
Cleaning applies a decision to a known or suspected issue. For example, if a data dictionary establishes one date format and incoming records use several, a cleaning step can convert them to the approved format. Duplicate records might be merged or removed if the business rule defines how duplicates should be handled. Missing values may be filled, left blank, quarantined, or removed depending on what they represent.
Cleaning can also standardize names, units, categories, and other representations so that equivalent values are treated consistently. The important point is that the canonical version must be approved. Converting units without knowing what the original unit was, for instance, could create a new error rather than fix one.
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Validation is rule-based checking
Validation compares data with explicit, verifiable expectations. Great Expectations describes an Expectation as “a verifiable assertion about data.” Expectations can be grouped into suites, and a Validation Definition connects a batch of data to the expectations used to check it. The result indicates how that batch compares with those rules. Great Expectations’ GX Core overview describes this validation model.
Rules can check structure, values, and relationships. A required column can be checked for existence; a field can be checked for an allowed type, range, or pattern; and a relationship can be tested across fields or tables. A passing check is evidence about those declared conditions, not a general certificate of correctness.
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Consider an identifier field: a validation rule could check whether values match the expected pattern, but a pattern match alone cannot establish that an identifier belongs to the correct person or account. Likewise, a numeric field can have the required type and still contain a value that is nonsensical for the business purpose. Good validation therefore reflects both technical requirements and meaningful business rules.
Which should you use?
Choose based on the problem: use cleaning when you know how to correct an issue, and validation when you need to determine whether data meets a requirement. Use both when you need to find, remediate, and verify data quality.
For an already-known inconsistency with an approved canonical form, cleaning is the direct action. For a requirement that must be enforced—such as a required field, an allowed category, or a valid relationship—validation is the right check. When a check finds a failure, investigate before treating it as a cleaning instruction: the value could be erroneous, an exception, or evidence that the rule needs review.
| Situation | Use | Example |
|---|---|---|
| You know a value is represented inconsistently and have an approved canonical form. | Cleaning | Normalize date formats or units according to a data dictionary. |
| A field must exist or have an allowed type. | Validation | Check required columns, column names, and data types. |
| A value must fit a meaningful range or pattern. | Validation, then remediation if it fails | Check an age range, identifier pattern, or accepted category; investigate a failure before changing it. |
| Records must agree across fields or tables. | Validation | Check that a transfer’s sent timestamp precedes its received timestamp, or that totals agree across tables. |
| Legacy data contains known errors. | Both | Profile for issues, apply documented cleaning rules, then validate before downstream use. |
| New data arrives continuously or in scheduled batches. | Repeated validation, with a defined failure policy | Warn and record metrics, drop invalid rows, or fail the flow according to operational risk. |
These choices depend on the intended use. A field that is optional in one context may be essential in another, and a range that is reasonable for one population or process may be wrong for another. Agree on what is acceptable before encoding a rule; otherwise, a check can consistently enforce an assumption that the business does not share.
Why schema checks alone are not enough
A schema check can confirm that expected columns exist and have expected types. It cannot establish that the values make sense for the business purpose. A numeric value can be in the correct type column and still be impossible; a text value can match an identifier pattern while referring to the wrong entity.
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Great Expectations’ schema guidance distinguishes schema validity from semantic correctness and points to checks such as field-level ranges and patterns. Its schema validation documentation is useful for structural checks, but validation rules should also represent the conditions that matter to downstream users.
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Think of schema checks as a necessary layer when structure matters, not as a substitute for every other kind of quality check. Depending on the intended use, a validation suite may also check allowed categories, expected patterns, uniqueness, freshness or timeliness, and whether related fields agree. Each rule should have a reason tied to how the data will be used; adding checks solely because they are easy to write can produce noise rather than useful assurance.
For data that relates across fields or tables, add integrity checks. Examples include checking timestamp ordering or reconciling totals between related tables. Great Expectations documents cross-field and cross-table integrity expectations, while noting that such checks can have performance implications on large datasets. Test the relevant workload rather than assuming every rule has negligible cost. Great Expectations’ data integrity guidance covers these checks.
Cross-table checks can be more demanding than a simple column-presence test, so the choice is not just whether a rule is expressible. Consider how often it must run and the size and shape of the relevant data, then test the check under the workload that matters. The available guidance does not establish a universal cost or performance figure; the practical implication is to measure your own case rather than assume all checks are free.
A practical workflow for using both
- Define what the data means and who owns those definitions. Record acceptable values, units, date conventions, required fields, and business rules in a data dictionary or equivalent. Get agreement from the people responsible for the domain; a tool cannot decide which values are correct for a particular use. Definitions should be clear enough that teams can distinguish a legitimate exception from an error.
- Profile and inspect before editing. Look for missing values, unexpected formats, possible duplicates, outliers, and broken relationships. Ask a data owner to distinguish actual errors from legitimate exceptions before changing records. Profiling helps locate candidates for review; it does not on its own identify the right correction.
- Clean only when the rule is understood. Standardize, correct, deduplicate, fill, quarantine, or remove data only when the outcome is defensible. Keep raw data or a record of changes when auditability matters; this is an operational practice, not an automatic feature of every platform. Document the decision so that later reviewers can understand why records were transformed or excluded.
- Validate structure and meaning. Check required fields and types, then add the relevant ranges, patterns, uniqueness, timeliness, referential integrity, and business logic. Select rules according to how the data will be used, rather than checking only what is easiest to express. A useful check states the expectation clearly enough that its result can guide a decision.
- Choose what a failed check does. Decide whether a failure should trigger a warning, be recorded for review, cause a row to be dropped or quarantined, or stop the affected flow. Make the consequence explicit before putting checks into production. A warning may suit a condition that needs monitoring; a critical condition may justify stopping the flow. The correct threshold depends on the risk of proceeding and the operational cost of interruption.
- Repeat checks after transformations and on later batches. A transformation can introduce a defect, and new source data can break a rule that previously passed. Run validation at the points where a failure would matter to downstream users. Validation can occur at ingestion, after transformation, and repeatedly for later batches; a one-time check of old data does not guarantee future batches meet the same expectations.
- Review the rules as the data changes. Business meanings and upstream schemas evolve. Update expectations deliberately, and investigate changed failure patterns to distinguish a source change from a stale rule. A rule should not be relaxed or altered simply to make a failure disappear; confirm that the underlying definition has changed and that downstream needs remain covered.
The workflow is iterative rather than a one-time cleanup project. A validation failure may reveal a source issue, an undocumented business exception, or a cleaning rule that was too broad. Feed that information back to the data owner, clarify the definition, then update the correction or check deliberately. This keeps the distinction intact: cleaning implements an approved correction, while validation tests the resulting data against an expectation.
What should happen when validation fails?
A failed check is a signal to apply an agreed policy, not a reason to silently rewrite data. The right response depends on the risk of letting invalid records continue and the cost of interrupting the flow.
- Warn and record metrics when the issue needs monitoring but does not justify stopping processing.
- Quarantine or drop invalid records when affected rows must not enter downstream use and the remaining data can safely proceed.
- Fail the flow when continuing would make the output unsafe or misleading.
- Investigate and remediate when the failure may reflect a source change, a bad record, or a rule that no longer matches the business meaning.
These are policy choices, not interchangeable technical settings. Keeping invalid records while reporting metrics allows processing to continue but leaves a follow-up need. Dropping records can protect downstream use but changes what data is available. Failing the flow prevents the affected output from proceeding but can interrupt work. Quarantine is useful when records need separate review, provided there is a clear path for investigating them. Pick the behavior according to the impact of each failure, and make the decision before relying on the check operationally.
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Databricks Lakeflow expectations document three invalid-record outcomes: keep records while reporting metrics, drop invalid records, or fail the flow. Expectations enforce data-quality conditions; they do not, by themselves, provide general pipeline orchestration. Microsoft Learn’s expectation recommendations describe these patterns. Product behavior can change, so check the current documentation for the runtime and version you use.
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Cleaning and validation are practices, not competing products. When choosing an implementation, consider whether it can express the rules you need, make failures actionable, and support repeatable checks with results that people can review.
- Purpose: Does the method correct data, detect rule violations, or support both stages? Be clear about which part of the workflow it covers; a validation framework is not automatically a cleaning system.
- Rule coverage: Does it check only structure, or also values, distributions, freshness, uniqueness, integrity, and domain-specific business rules? Cross-field and cross-table requirements may matter more than a long list of basic schema assertions.
- Failure handling: Can it report, warn, retain, quarantine or drop records, or stop a pipeline—and what are the consequences of each choice? Match actions to risk rather than using one response for every failed assertion.
- Repeatability and traceability: Can rules be reused, results inspected, and rule changes reviewed? Great Expectations documents suites, validation results, Checkpoints, and actions. Databricks recommends keeping expectation definitions separate from pipeline logic for portability and maintenance.
- Scale and operating cost: Cross-table checks and large datasets can affect performance. Test the rules against the workload that matters to your system. The available sources do not establish a universal price or quantified performance difference between approaches.
- Business ownership: Can subject-matter owners define correct values and approve remediation? Data quality depends on intended use and business logic, decisions a tool cannot make on its own.
It is also useful to distinguish repeatable evidence from a claim of perfection. Reusable expectations and inspectable results make it easier to see how a batch compared with the rules in force at the time. They cannot guarantee that the rules cover every defect, and rules themselves need review when sources, schemas, or business definitions change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Examples of validation tools in context
Great Expectations is one example of validation software: its documentation describes defining expectations, validating batches, reviewing results, and using Checkpoints in production workflows. It describes connections to dataframes, files, and SQL data sources. Those capabilities are an example, not a reason every team needs a separate platform. The GX Core overview explains the framework’s model.
In that model, assertions are grouped into suites and used to check a batch through a Validation Definition. Production Checkpoints and actions can support recurring validation workflows and operational follow-up, such as processing results or notifications. This can make checks repeatable, but teams still have to decide what the expectations should say and what failure means for their own data.
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A Microsoft Fabric tutorial dated August 15, 2025 demonstrates validating constraints on a Fabric workspace dataset using SemPy and Great Expectations, then reviewing results with a Checkpoint. It illustrates one ecosystem-specific implementation rather than a general recommendation. The MicrosoftDocs tutorial provides the steps and context.
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Databricks Lakeflow documentation provides another scoped example, describing expectation behaviors for keeping records with metrics, dropping invalid records, or failing a flow. Consult current product documentation for the version and runtime in use because implementation details can change. In either case, compare approaches on purpose, rule coverage, failure behavior, repeatability, scale, and business ownership—not on the assumption that cleaning and validation are rival products.
In either case, the core design decision is the same: define rules that reflect the data’s intended use, decide how to handle failures, and use cleaning only where the correction is known.
Frequently Asked Questions
Should I clean data before or after validation?
A dependable workflow usually defines what valid data means, profiles and inspects the data, applies documented cleaning rules, and then validates the transformed result. Validation can also run at ingestion and again on later batches, so it is not limited to a single point before or after cleaning.
Does passing data validation prove that the data is correct?
No. It shows that the data met the expectations that were checked. Schema checks alone do not establish semantic correctness, and even broader rules cannot prove that every defect has been found. Include meaningful ranges, patterns, relationships, and business rules where they matter to the data’s use.
Should a failed validation check automatically change the data?
Not unless an approved cleaning rule specifies the correction. A failure may indicate a bad record, a legitimate exception, an upstream change, or a stale expectation. Investigate the cause before rewriting values; if no defensible correction is known, flagging, quarantining, or otherwise handling the record may be more appropriate.
What should a pipeline do with invalid records?
Choose a failure policy based on the risk of continuing and the cost of stopping. Options documented for Databricks Lakeflow expectations include keeping records while reporting metrics, dropping invalid records, or failing the flow. Teams may also route records for separate review; make the policy explicit rather than silently changing data.
Do I need a separate validation platform?
Not necessarily. Great Expectations is one example of software for defining expectations, validating batches, and reviewing results, but available guidance does not establish that every team needs a separate platform. Choose an implementation based on the rules it can express, how it handles failures, repeatability and traceability, workload scale, and whether business owners can define the requirements.
How often should data be validated?
Validate at points where a failure would matter, such as at ingestion, after meaningful transformations, and on later scheduled or continuous batches. New data can break a rule that previously passed, and a transformation can introduce a defect. Review rules as upstream schemas and business meanings change.
The Bottom Line
Use cleaning to make an approved correction; use validation to check explicit requirements. Most teams need both: define what valid means, inspect and clean only with defensible rules, validate structure and business meaning, and choose in advance whether failures should warn, be isolated or dropped, or stop a flow. A passing check is evidence that its declared rules passed—not proof that the data is universally correct.
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