Version: Next

Check Types

Teckel defines 10 check types covering different quality dimensions. Each check has a type field that determines how validation is performed.

Schema

Validates the structural shape of the dataset: which columns must exist, which must not, and expected data types.

- type: schema
  columns:
    required: [id, name, email]
    forbidden: [ssn, credit_card]
  types:
    id: integer
    amount: double

Field	Type	Required	Description
`columns.required`	List[string]	No	Columns that MUST be present in the dataset.
`columns.forbidden`	List[string]	No	Columns that MUST NOT be present. Supports glob patterns (e.g., `pii_*`).
`types`	Map[string, string]	No	Expected data types per column.

Completeness

Validates that required values are present (non-null) in a column.

- type: completeness
  column: email
  threshold: 0.95

Field	Type	Required	Default	Description
`column`	Column	Yes	—	Column to check.
`threshold`	double	No	`1.0`	Minimum fraction of non-null values (0.0 to 1.0).

A threshold of 1.0 means all values must be non-null. A threshold of 0.95 allows up to 5% nulls.

Uniqueness

Validates that values are unique across the dataset. Supports composite keys.

- type: uniqueness
  columns: [customer_id]

# Composite uniqueness
- type: uniqueness
  columns: [order_id, line_item_id]
  threshold: 1.0

Field	Type	Required	Default	Description
`columns`	NonEmptyList[Column]	Yes	—	Columns forming the uniqueness key.
`threshold`	double	No	`1.0`	Minimum fraction of unique values.

Validity

Validates that values conform to expected formats, ranges, or sets. This is the most versatile check type, supporting five validation modes.

Accepted Values

Restrict a column to a fixed set of allowed values.

- type: validity
  column: status
  acceptedValues: [active, inactive, pending]

Range

Enforce numeric bounds. Use strictMin and strictMax for exclusive bounds.

- type: validity
  column: amount
  range:
    min: 0
    max: 1000000
    strictMin: true       # > 0, not >= 0

RangeSpec fields:

Field	Type	Default	Description
`min`	number	—	Minimum value (inclusive by default).
`max`	number	—	Maximum value (inclusive by default).
`strictMin`	boolean	`false`	Use exclusive lower bound (`>` instead of `>=`).
`strictMax`	boolean	`false`	Use exclusive upper bound (`<` instead of `<=`).

Pattern

Validate values against a regular expression.

- type: validity
  column: email
  pattern: "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"

Format

Validate values against a built-in format validator.

- type: validity
  column: phone
  format: phone

Length

Enforce string length bounds (inclusive).

- type: validity
  column: code
  lengthBetween: [3, 10]

Validity Fields Summary

Field	Type	Required	Default	Description
`column`	Column	Yes	—	Column to validate.
`acceptedValues`	List[string]	No	—	Allowed values (enumeration).
`range`	RangeSpec	No	—	Numeric range constraint.
`pattern`	string	No	—	Regular expression pattern.
`format`	string	No	—	Built-in format name.
`lengthBetween`	[integer, integer]	No	—	`[min, max]` string length (inclusive).
`threshold`	double	No	`1.0`	Minimum fraction of valid values.

Built-in Format Validators

Implementations MUST support these format validators:

Format	Description
`email`	RFC 5322 email address.
`uuid`	UUID (any version).
`url`	Valid URL with scheme.
`ipv4`	IPv4 address.
`ipv6`	IPv6 address.
`date`	ISO 8601 date (`YYYY-MM-DD`).
`timestamp`	ISO 8601 timestamp.
`phone`	International phone number format.

Statistical

Validates statistical properties of numeric columns: mean, standard deviation, min, max, sum, and quantiles.

- type: statistical
  column: salary
  mean:
    between: [40000, 120000]
  stdev:
    max: 50000
  quantiles:
    0.25: { min: 30000 }
    0.50: { between: [45000, 80000] }
    0.75: { max: 150000 }

Field	Type	Required	Description
`column`	Column	Yes	Numeric column to analyze.
`mean`	BoundSpec	No	Expected mean bounds.
`min`	BoundSpec	No	Expected minimum value bounds.
`max`	BoundSpec	No	Expected maximum value bounds.
`sum`	BoundSpec	No	Expected sum bounds.
`stdev`	BoundSpec	No	Expected standard deviation bounds.
`quantiles`	Map[double, BoundSpec]	No	Quantile bounds. Key is the percentile (0.0 to 1.0).

BoundSpec fields:

Field	Type	Description
`min`	number	Minimum expected value.
`max`	number	Maximum expected value.
`between`	[number, number]	`[min, max]` range (inclusive).

Volume

Validates the size of the dataset by row count and/or column count.

- type: volume
  rowCount:
    between: [1000, 100000]

Field	Type	Required	Description
`rowCount`	BoundSpec	No	Expected row count bounds.
`columnCount`	BoundSpec	No	Expected column count bounds.

Freshness

Validates data recency by checking a timestamp column against the current time.

- type: freshness
  column: updated_at
  maxAge: "PT24H"

Field	Type	Required	Description
`column`	Column	Yes	Timestamp column to check.
`maxAge`	string	Yes	ISO 8601 duration. The maximum value in the column must be within this duration of the current time.

Common durations:

PT1H — 1 hour
PT24H — 24 hours
P1D — 1 day
P7D — 7 days

Referential

Validates referential integrity — that values in a column exist in another asset (foreign key relationship).

- type: referential
  column: customer_id
  reference:
    asset: customers
    column: id
  threshold: 1.0

Field	Type	Required	Default	Description
`column`	Column	Yes	—	Column in the target asset.
`reference.asset`	AssetRef	Yes	—	The referenced asset.
`reference.column`	Column	Yes	—	Column in the referenced asset.
`threshold`	double	No	`1.0`	Minimum fraction of values that must exist in the reference.

Cross-Column

Validates relationships between columns within the same dataset.

- type: crossColumn
  condition: "start_date <= end_date"
  description: "Start date must not be after end date"

Field	Type	Required	Default	Description
`condition`	Condition	Yes	—	Boolean expression referencing columns in the target asset.
`description`	string	No	—	Human-readable description.
`threshold`	double	No	`1.0`	Minimum fraction of rows satisfying the condition.

Custom

Validates using an arbitrary boolean expression evaluated per row.

- type: custom
  condition: "amount > 0 AND currency IS NOT NULL"
  description: "All transactions must have positive amount and currency"

Field	Type	Required	Default	Description
`condition`	Condition	Yes	—	Boolean expression evaluated per row.
`description`	string	No	—	Human-readable description.
`threshold`	double	No	`1.0`	Minimum fraction of rows passing.

Check Result Model

Each check execution produces a result object with the following fields:

Field	Type	Description
`suite`	string	Suite name.
`check_type`	string	Check type identifier.
`target`	string	Asset name.
`column`	string?	Column name (if applicable).
`status`	string	Outcome: `"pass"`, `"warn"`, `"fail"`, or `"error"`.
`severity`	string	Configured severity level.
`observed_value`	any	Computed metric value.
`expected`	string	Threshold or assertion description.
`failing_rows`	integer?	Number of rows failing (for row-level checks).
`failing_percent`	double?	Percentage of rows failing.
`description`	string?	Check description.
`timestamp`	string	ISO 8601 execution timestamp.

Comprehensive Example

The following suite combines multiple check types to validate an orders dataset end-to-end.

quality:
  - suite: orders-full-validation
    description: "End-to-end quality validation for the orders dataset"
    target: orders
    severity: error
    checks:
      # 1. Schema: enforce required columns and types
      - type: schema
        columns:
          required: [order_id, customer_id, amount, status, created_at]
          forbidden: [internal_id, debug_flag]
        types:
          order_id: string
          amount: double
          created_at: timestamp

      # 2. Completeness: critical fields must not be null
      - type: completeness
        column: customer_id
        threshold: 1.0

      - type: completeness
        column: email
        threshold: 0.95
        severity: warn

      # 3. Uniqueness: order_id is the primary key
      - type: uniqueness
        columns: [order_id]

      # 4. Validity: status must be from known values
      - type: validity
        column: status
        acceptedValues: [pending, confirmed, shipped, delivered, cancelled]

      # 5. Validity: amount must be positive
      - type: validity
        column: amount
        range:
          min: 0
          strictMin: true

      # 6. Validity: email format
      - type: validity
        column: email
        format: email
        threshold: 0.99
        severity: warn

      # 7. Statistical: amount distribution sanity
      - type: statistical
        column: amount
        mean:
          between: [50, 500]
        quantiles:
          0.99: { max: 10000 }

      # 8. Volume: expected dataset size
      - type: volume
        rowCount:
          between: [100, 10000000]

      # 9. Freshness: data should be recent
      - type: freshness
        column: created_at
        maxAge: "PT48H"

      # 10. Referential: customer_id must exist in customers
      - type: referential
        column: customer_id
        reference:
          asset: customers
          column: id

      # 11. Cross-column: business logic
      - type: crossColumn
        condition: "shipped_at IS NULL OR shipped_at >= created_at"
        description: "Shipping date must be after order creation"

      # 12. Custom: composite business rule
      - type: custom
        condition: "amount > 0 AND currency IS NOT NULL"
        description: "All transactions must have positive amount and currency"
        threshold: 0.99
        severity: warn

Schema​

Completeness​

Uniqueness​

Validity​

Accepted Values​

Range​

Pattern​

Format​

Length​

Validity Fields Summary​

Built-in Format Validators​

Statistical​

Volume​

Freshness​

Referential​

Cross-Column​

Custom​

Check Result Model​

Comprehensive Example​