Financial Transaction Quality
freeValidate transaction data — amounts, currencies, account references, dates, and reconciliation checks for payment processing.
Checks included (19)
Non-Negative Values
Validates that a numeric column contains no negative values. Common for quantities, counts, amounts, durations, and other measures that should never be negative.
Amount Limit
Validates that financial amounts do not exceed a configurable limit. Catches data entry errors, currency conversion issues, and fraudulent transactions. Supports both upper and optional lower bounds.
Value In Range
Validates that all values in a numeric column fall within a specified minimum and maximum range (inclusive). Catches data entry errors, unit conversion issues, and out-of-bounds values.
Z-Score Outlier Detection
Detects statistical outliers by checking if values fall within X standard deviations of the mean (z-score method). Values with |z-score| > threshold are flagged as potential outliers. Useful for detecting data entry errors, measurement anomalies, and distribution shifts.
Date Not In Future
Validates that a date or timestamp column contains no values in the future. Catches data entry errors, timezone issues, and ETL bugs that produce future-dated records for columns like birth_date, transaction_date, or created_at.
ISO Currency Code Validation(currency_code)
Validates that values are valid ISO 4217 currency codes (e.g., USD, EUR, GBP, JPY)
IBAN Format Validation(iban)
Validates International Bank Account Number format — 2-letter country code, 2 check digits, and up to 30 alphanumeric characters
ISO Country Code Validation(country_code)
Validates that values are valid ISO 3166-1 alpha-2 country codes (e.g., US, GB, DE, FR)
Sum Matches Total
Asserts that the sum of detail/line-item rows matches the corresponding header or total row value. Validates financial reconciliation, order totals, and any parent-child numeric relationships where parts must equal the whole.
Date Order Valid
Asserts that a start date column is always before or equal to an end date column for every row. Catches data entry errors, timezone conversion bugs, or ETL transformation issues that invert temporal ordering.
Enum Value Valid
Asserts that all values in a column belong to a predefined set of allowed values. Catches typos, unexpected category values, or upstream system changes that introduce new enum variants without coordination.
Column Not Null
Asserts that a specified column contains no null values. This is the most fundamental completeness check — every row must have a value present in the target column.
Column Completeness Threshold
Asserts that a column meets a minimum completeness threshold, measured as the percentage of non-null values. Useful when some nulls are acceptable but the overall population rate must stay above a defined level (e.g., 95%).
Column Unique
Validates that all non-null values in a specified column are unique. Useful for natural keys, email addresses, identifiers, and any column where duplicates indicate a data quality issue.
Composite Unique
Validates that the combination of specified columns forms a unique key. Ensures no two rows share the same values across all listed columns, enforcing composite key integrity.
Foreign Key Valid
Validates that all non-null values in a foreign key column exist in the referenced parent table's primary key column. Detects orphaned references that break referential integrity.
Cross-Table Count Match
Validates that row counts or aggregated values match between related tables. For example, the number of order line items should match the item_count on the order header, or the sum of transaction amounts should match the account balance.
Table Freshness
Asserts that a table has been updated within the specified number of hours. Uses the table's metadata (last modified timestamp) or a designated timestamp column to verify data is fresh and pipelines are running on schedule.
Row Count Growth
Asserts that the current row count has not decreased more than the specified percentage compared to the previous run's row count. Detects accidental data loss, failed incremental loads, or unintended deletions between pipeline runs.