Use data cleaning to correct, standardize, or remove known data problems. Use data validation to check whether data meets explicit requirements. Most dependable workflows need both: define what counts as valid, clean issues using documented rules, then validate the result and check later batches.
What is the difference between data cleaning and data validation?
Data cleaning changes data. It addresses observed problems such as inconsistent formats, duplicates, missing or malformed values, and records that need correction or removal. A cleaning tool can identify anomalies, but it cannot know the right value without a rule based on the data’s intended meaning.
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Data validation checks data against declared expectations. Great Expectations describes an Expectation as “a verifiable assertion about data”; a suite groups expectations, and a Validation Definition connects a batch of data to the checks that should be run against it. The results show whether the batch met those expectations, not whether every possible problem has been found. Great Expectations: GX Core overview
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThe distinction matters because validation detects a mismatch but does not necessarily fix it. If a date uses an unexpected format, a check can flag the format; cleaning applies an approved conversion. Microsoft Learn treats profiling, cleansing, validating, and monitoring as separate data-quality activities. Microsoft Learn: Best practices for data and AI governance
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Think of cleaning as a decision about what to do with a known issue, and validation as a test of whether the data meets a stated condition. For example, if a data dictionary specifies one standard date format, cleaning can convert known alternate formats to that standard. A validation rule can then check that dates follow the accepted format. If a date fails, the check identifies a problem; a person or documented remediation rule still needs to determine whether it should be converted, corrected, quarantined, or left as an exception.
This separation helps prevent a common mistake: treating every failed test as an instruction to rewrite the data. A failed range or pattern check can be evidence of a typo, but it can also be a legitimate exception or a sign that the expectation is outdated. Validation provides evidence against the rules in force; cleaning should follow an approved interpretation of the data.
Which one should you use?
Choose based on the problem: use cleaning when you know what correction is appropriate; use validation when you need to test whether data meets a requirement. Use both when you need to find, remediate, and prevent issues.
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| Situation | Use | Example |
|---|---|---|
| Values are inconsistently represented and an approved canonical form exists | Cleaning | Normalize date formats or units according to a data dictionary. |
| A field must be present or use 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 before changing a failing value. |
| Records must agree across fields or tables | Validation | Check that a transfer’s sent timestamp precedes its received timestamp, or compare matching totals across tables. |
| Legacy data has known errors | Both | Profile for issues, apply documented cleaning rules, and validate the result before downstream use. |
| New data arrives continuously or in scheduled batches | Repeated validation, with a chosen failure policy | Warn and record metrics, remove invalid rows, or fail a flow according to operational risk. |
The table is a guide to the type of work, not a rule that the practices must happen only once or in a fixed order. A team may validate incoming data to identify problems, clean the issues it understands, and validate again before releasing the transformed data. It may then repeat validation for each scheduled or continuous batch. The appropriate sequence depends on where issues are discovered and where downstream consumers need assurance.
Use cleaning when a correction is already justified, such as converting approved unit variants to a standard unit. Use validation when the question is whether a condition holds, such as whether a required field exists or a relationship between two timestamps is valid. When a check fails and the right correction is not known, investigate instead of silently substituting a value.
Why a passing validation check is not proof that data is correct
A dataset can have the expected columns and data types while containing values that are wrong for their intended meaning. Schema checks answer structural questions—such as whether a required column exists—not whether a value makes sense in context. Great Expectations explicitly distinguishes schema validation from semantic correctness. Great Expectations: Validate data schema with GX
For meaningful assurance, add checks that reflect how the data will be used. Depending on the dataset, that can include accepted ranges, patterns, categories, uniqueness, timeliness, relationships between fields, or referential integrity across tables. For instance, a timestamp column can have the correct type but still contain an event time that occurs before the event that supposedly caused it. Integrity checks can test such relationships. Great Expectations: Validate data integrity with GX
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Validation can only assess the expectations that have been defined. A passed result means the checked rules passed; it does not establish that the rules cover every defect or that the rules themselves reflect the right business meaning.
It is therefore useful to think about several layers of checks. Structural checks can establish whether required columns are present and use allowed types. Field-level checks can test whether values match a pattern, fall within a range, or belong to an accepted category. Relationship checks can ask whether fields agree with one another or whether records in separate tables maintain the required integrity. The right set depends on the data’s intended use; adding checks that do not express a meaningful requirement can create noise without making the result more trustworthy.
For example, a dataset can pass a type check for a transfer timestamp while still violating the business relationship that the sent time should precede the received time. Likewise, two tables can each have the expected schema while their corresponding totals fail to agree. These examples show why a schema check alone is not a comprehensive quality assessment. They also show why rule ownership matters: the team needs a subject-matter decision about which relationships are important and what counts as an acceptable exception.
A practical workflow for cleaning and validating data
- Define meaning and ownership. Record acceptable values, units, date conventions, required fields, and business rules in a data dictionary or equivalent. Agree with the people responsible for the data on what counts as a legitimate value or exception. Microsoft recommends standard definitions and consistent formats, names, and units. Microsoft Learn: Best practices for data and AI governance
- Profile and inspect before editing. Look for missing values, unexpected formats, likely duplicates, outliers, and broken relationships. Treat these as findings to investigate: an unusual value may be a real exception rather than an error.
- Clean only when the rule is understood. Standardize, correct, deduplicate, fill, quarantine, or remove data only when the outcome is defensible. Preserve the raw data or record changes when traceability matters, so a later user can see what was altered and why.
- Validate structure and meaning. Check required fields and types, then add relevant range, pattern, uniqueness, timeliness, integrity, and business-logic expectations. Include cross-field or cross-table checks when those relationships matter to downstream use. Great Expectations: Validate data integrity with GX
- Decide what a failed check does. Choose a response that matches the risk: retain the record and log a warning, drop invalid records, or fail the affected flow. A warning preserves pipeline progress but leaves bad data available to downstream users; dropping records can lose useful information; failing a flow can delay delivery but prevent untrusted data from moving forward.
- Run checks again after meaningful transformations and on later batches. A transformation can introduce errors, and upstream data can change. Great Expectations documents reusable validation definitions and Checkpoints for production workflows, with actions such as notifications. Great Expectations: GX Core overview
- Review expectations as the data changes. Upstream schemas and business meanings can change. Update rules deliberately; a sudden rise in failures may signal either a source change or an expectation that no longer fits the data.
The first step is more than recording column names. Data owners need to agree on definitions that affect interpretation: units, date conventions, required fields, acceptable values, and relevant business logic. Standard definitions and consistent names, formats, and units make it easier to distinguish a real defect from a representation that is simply different from what a downstream process expects.
Profiling comes before editing because it helps reveal what is present without assuming that every irregularity is wrong. Missingness, duplicate candidates, unexpected formats, outliers, and relationship breaks are investigation leads. A duplicate candidate, for example, should not automatically be removed until the team has established whether it represents a repeated record or a legitimate event. Similarly, an unusual value should be checked with the data owner before it is corrected or discarded.
When cleaning is justified, use the known rule consistently and make the action traceable where auditability matters. The correction might be standardization, a defensible fill, deduplication, quarantine, or removal. Preserve the raw data or record what changed when a later user needs to understand the transformation. This is practical workflow guidance, not a guarantee that any particular tool automatically captures a complete change history.
Validation should match the consequences of using the data. A required-column check may be essential at ingestion. A business relationship check may be most useful after data from multiple sources has been combined. Checks can run at ingestion, after transformation, or on later batches; the workflow should place them where they can catch relevant problems before those problems affect a downstream use.
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After adding or changing a transformation, run the relevant checks again. Cleaning itself can produce an unexpected result, and later source batches can differ from earlier ones. Repeated validation helps reveal whether a known issue has returned, a new issue has appeared, or an upstream schema has changed. A validation result is most operationally useful when the team knows which batch was checked, which expectations applied, and what follow-up is required.
How should you handle validation failures?
A failed check is a signal to apply a policy, not a reason to silently alter the value. First determine whether the failure is a genuine data defect, a legitimate exception, or a stale or overly strict rule. Then use an action consistent with the consequences of passing the record downstream.
For example, Databricks Lakeflow expectations support different outcomes for invalid records: keep them while recording metrics under warning behavior, drop them, or fail a flow. These are operational choices, not automatic corrections; expectations do not perform general pipeline orchestration by themselves. Check the current product documentation for the runtime and version in use because implementation details can change. Microsoft Learn: Expectation recommendations and advanced patterns
- Warn and retain: useful when visibility is important and the pipeline can safely continue, but downstream consumers must know invalid records remain present.
- Drop invalid rows: prevents failing records from continuing, but risks losing valid edge cases if the rule is wrong.
- Fail the flow: appropriate when continuing would create unacceptable risk, while requiring a process to investigate and recover the blocked batch.
There is no universally correct failure action. A warning can preserve delivery and provide metrics, but it does not remove the record or prevent downstream use. Dropping a failing row keeps it out of the continuing flow, but an inaccurate expectation may cause useful or legitimate data to be lost. Failing a flow can prevent untrusted data from moving onward, but it can also delay delivery and requires a way to investigate and recover the blocked batch. Match the choice to operational risk, and make sure consumers understand what the selected behavior means.
Keep the distinction between the check and the response clear. An expectation says what condition is being tested. The failure policy says what happens when a record or batch does not meet it. Neither by itself decides what a value should have been. If remediation is appropriate, it should be based on a documented, defensible rule; otherwise, investigation or escalation to a data owner is the safer next step.
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What to compare when choosing validation tools or approaches
Cleaning and validation are practices, not competing products. A tool or approach may support one or both. Evaluate it against the work your data requires:
- Purpose: Does it correct values, detect rule violations, or support both?
- Rule coverage: Can it check structure as well as value ranges, patterns, freshness, uniqueness, integrity across fields or tables, and business-specific rules? Schema checks and integrity checks cover different kinds of expectations.
- Failure handling: Can results warn, retain, quarantine or drop records, or stop a pipeline? Consider what each outcome means for data loss, delay, and downstream risk. Databricks expectation patterns
- Repeatability and traceability: Can teams reuse rules, inspect results, and review rule changes? Great Expectations documents suites, validation results, Checkpoints, and actions. Databricks recommends storing expectation definitions separately from pipeline logic to support portability and maintenance. Great Expectations overview · Databricks expectation patterns
- Scale and operating cost: Cross-table checks and large datasets can affect performance. Test the rules against the workload and data volumes that matter rather than assuming every check is free. Great Expectations: Validate data integrity with GX
- Business ownership: Can subject-matter owners define what correct means and approve remediation? A tool can enforce a rule, but it cannot decide the intended business meaning for you.
These criteria help compare implementations without assuming that “cleaning” and “validation” are rival categories. A tool that detects a failed rule may not provide the corrective action a team needs. A cleaning process may standardize values but still need independent checks to show that the output meets the conditions required downstream. Identify which capability is missing from the workflow rather than selecting a product based on its label.
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Rule coverage also deserves a practical review. A basic schema check may be enough for one narrow handoff, while a business-critical dataset may need field patterns, ranges, timeliness, uniqueness, relationships between fields, or checks across tables. More checks are not automatically better: prioritize rules that express a real requirement for the intended use, and evaluate whether cross-table checks and larger workloads affect performance. Test relevant workload and data volumes instead of assuming each expectation is free.
Repeatability matters when checks run on recurring batches. Teams should be able to reuse the rules, inspect validation results, and review changes to those rules. Great Expectations documents suites, validation results, Checkpoints, and actions; Databricks recommends separating expectation definitions from pipeline logic to support portability and maintenance. The business meaning still needs human ownership: subject-matter owners must agree on what counts as correct and approve remediation rules.
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Great Expectations
Great Expectations describes a framework for defining expectations and validating batches, with validation results and production Checkpoints. Its documentation describes connections to dataframes, files, and SQL data sources. It is one example of validation software, not a requirement that every team adopt a separate platform. Great Expectations: GX Core overview
In its documented model, an Expectation is a verifiable assertion, a suite groups assertions, and a Validation Definition associates a batch with the expectations to run. The validation result lets a team review how that batch compared with the declared rules. Production Checkpoints can support recurring workflows, and actions can be used for follow-up such as notifications. This makes the framework an example of repeatable validation, not a substitute for deciding what the rules should mean.
Microsoft Fabric with SemPy and Great Expectations
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 a workflow within that ecosystem; it is not a universal setup requirement. MicrosoftDocs: Tutorial: Validate data using SemPy and Great Expectations (GX)
Use this as a concrete example of validation in a particular environment, rather than as evidence that every team needs the same components. Whether a separate framework is useful depends on the team’s environment, desired rule coverage, repeatability, and operational needs.
Databricks Lakeflow expectations
Microsoft Learn documents Lakeflow pipeline expectations with configurable handling for invalid records, including warning, dropping records, or failing a flow. Because product behavior can evolve, confirm the current documentation for the specific runtime and version you deploy. Microsoft Learn: Expectation recommendations and advanced patterns
This example illustrates that validation tools may provide a choice about how a pipeline responds to invalid data. Those actions should not be confused with general orchestration or with automated cleaning: expectations enforce data-quality rules, while teams still decide how failures are handled and whether any correction is justified.
Common mistakes to avoid
- Changing an outlier just because it is unusual: investigate whether it is an error or a valid exception before correcting or removing it.
- Treating schema validity as semantic correctness: correct types and columns do not prove values make sense for the business task.
- Writing checks without defining ownership: teams need agreement on acceptable values and who can approve changes to rules.
- Ignoring the consequences of failure: retaining, dropping, and stopping data flows have different risks; select deliberately rather than relying on a default.
- Validating only once: transformations and later input batches can introduce new issues, so place checks at relevant stages and run them repeatedly.
Another mistake is treating validation as a blanket guarantee. A passed check describes the declared rules and the data batch tested; it does not say that every possible defect was tested for, or that the expectations captured every business requirement. Review the rules when the source schema or business meaning changes, and investigate new failure patterns rather than assuming every failure is a data defect.
Finally, avoid adding complexity without a use case. The available product documentation does not establish that one named tool or workflow produces a quantified improvement, nor does it provide a universal price or cost saving. Compare the coverage and operating consequences relevant to your workload, and test performance where large datasets or cross-table checks matter.
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Can data be valid but still be wrong?
Yes. A dataset can have the expected columns and data types while containing values that are semantically wrong for their intended use. Schema checks establish structural conditions; add field-level and relationship checks that reflect business meaning, while recognizing that no set of expectations proves every value is true.
Should I clean data before or after validation?
There is no single mandatory order. Define what valid means first, profile the data, clean known issues using documented decisions, and validate the result. Checks can also run at ingestion, after transformations, and on later batches so issues are caught where they matter.
What should I do when a validation check fails?
Investigate whether the failure is a genuine defect, a legitimate exception, or a stale or overly strict rule. Then follow an intentional policy: warn and retain the record, drop it, or fail the affected flow. Do not silently change a value unless an approved remediation rule supports the correction.
Does data validation automatically fix bad data?
Not necessarily. Validation tests data against expectations and reports how it compares with them. Cleaning performs corrections or standardization when the appropriate outcome is understood; a failed check alone does not determine the right value.
Do I need a separate validation tool?
Not always. Great Expectations and Databricks Lakeflow are documented examples of validation capabilities, and Microsoft Fabric has a tutorial using SemPy with Great Expectations. Decide based on needed rule coverage, failure handling, repeatability, traceability, scale, and your existing environment rather than assuming every team needs a separate platform.
The Bottom Line
Use data cleaning to make approved corrections and data validation to test explicit requirements. Define the rules with data owners, investigate anomalies before changing them, and validate structure as well as relevant business relationships. Choose a deliberate response to failures and repeat checks after transformations and on new batches. A passing result means the declared checks passed—not that the data is proven true.
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