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Data types supported for model columns in Fiddler. This enum defines the supported data types for model schema columns. Data types determine how Fiddler processes, validates, and monitors individual columns in your model’s input and output data. Type Categories:
  • Numeric: FLOAT, INTEGER - enable statistical analysis
  • Categorical: BOOLEAN, CATEGORY - enable distribution analysis
  • Textual: STRING - enable text-based monitoring
  • Temporal: TIMESTAMP - enable time-based analysis
  • Vector: VECTOR - enable embedding-based monitoring

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Examples

Defining column data types in model schema: 
from fiddler import Column, DataType

# Define columns with appropriate data types
columns = [
    Column(name='age', data_type=DataType.INTEGER),
    Column(name='income', data_type=DataType.FLOAT),
    Column(name='is_member', data_type=DataType.BOOLEAN),
    Column(name='category', data_type=DataType.CATEGORY),
    Column(name='description', data_type=DataType.STRING),
    Column(name='created_at', data_type=DataType.TIMESTAMP),
    Column(name='embedding', data_type=DataType.VECTOR)
]

# Create model schema
schema = fdl.ModelSchema(columns=columns)
Data type validation and monitoring: 
# Numeric types enable statistical monitoring
if column.data_type.is_numeric():

    # Statistical drift detection available
    # Range validation enabled
    # Distribution analysis supported
    pass

    # Categorical types enable distribution monitoring
    if column.data_type.is_bool_or_cat():

        # Category distribution tracking
        # New category detection
        # Frequency analysis
        pass

        # Vector types enable embedding monitoring
        if column.data_type.is_vector():

            # Embedding drift detection
            # Clustering analysis
            # Dimensionality monitoring
            pass
Choose data types that accurately represent your data for optimal monitoring and validation. Incorrect data types may lead to inappropriate metrics or monitoring failures.

​
FLOAT = ‘float’

Floating-point numerical values. Used for continuous numerical data with decimal precision. Enables comprehensive statistical analysis and numerical drift detection. Characteristics:
  • Decimal precision values
  • Statistical distribution analysis
  • Range and outlier detection
  • Correlation analysis support
Monitoring features:
  • Mean, median, standard deviation tracking
  • Distribution drift detection (KS test, PSI)
  • Range violation alerts
  • Outlier detection and analysis
Typical use cases:
  • Prices, costs, revenues
  • Probabilities and confidence scores
  • Measurements and sensor readings
  • Performance metrics and ratios
  • Model prediction scores
Validation: Numeric range checks, NaN detection

​
INTEGER = ‘int’

Integer numerical values. Used for whole number data without decimal places. Supports numerical analysis while recognizing discrete nature of integer data. Characteristics:
  • Whole number values only
  • Discrete distribution analysis
  • Count-based statistics
  • Range validation
Monitoring features:
  • Count distribution tracking
  • Range violation detection
  • Discrete value frequency analysis
  • Statistical drift detection
Typical use cases:
  • Counts and quantities
  • Age, years, days
  • IDs and identifiers (when numeric)
  • Ranking positions
  • Categorical codes (when numeric)
Validation: Integer format checks, range validation

​
BOOLEAN = ‘bool’

True/false binary values. Used for binary flag data with exactly two possible values. Enables binary distribution analysis and proportion tracking. Characteristics:
  • Exactly two values (True/False, 1/0, Yes/No)
  • Binary distribution analysis
  • Proportion-based metrics
  • Simple categorical handling
Monitoring features:
  • True/False ratio tracking
  • Binary distribution drift
  • Proportion change detection
  • Flag frequency analysis
Typical use cases:
  • Feature flags and indicators
  • Binary classifications
  • Yes/No survey responses
  • Membership status
  • Activation states
Validation: Binary value format checks

​
STRING = ‘str’

Text string values. Used for textual data of variable length. Supports text-based analysis and can be combined with text embeddings for advanced monitoring. Characteristics:
  • Variable length text
  • Text-based analysis
  • String pattern detection
  • Encoding-aware processing
Monitoring features:
  • Length distribution tracking
  • Pattern and format analysis
  • Text embedding integration
  • String uniqueness analysis
Typical use cases:
  • Names and descriptions
  • Comments and reviews
  • URLs and paths
  • Free-form text inputs
  • JSON or XML strings
Special considerations:
  • Can be converted to embeddings for semantic monitoring
  • Supports text enrichment features
  • May require text preprocessing

​
CATEGORY = ‘category’

Categorical values with limited distinct options. Used for data with a finite set of possible values or categories. Enables categorical distribution analysis and new category detection. Characteristics:
  • Limited set of possible values
  • Categorical distribution tracking
  • Category frequency analysis
  • New category detection
Monitoring features:
  • Category distribution drift
  • New/missing category alerts
  • Frequency change detection
  • Category proportion analysis
Typical use cases:
  • Product categories
  • Geographic regions
  • Status codes
  • Demographic categories
  • Classification labels
Best practices:
  • Use for data with < 1000 unique values
  • Consider STRING type for high-cardinality categories
  • Define expected categories during schema creation

​
TIMESTAMP = ‘timestamp’

Date and time values. Used for temporal data including dates, times, and timestamps. Enables time-based analysis and temporal pattern detection. Characteristics:
  • Date/time information
  • Temporal ordering
  • Time-based aggregations
  • Timezone awareness
Monitoring features:
  • Temporal pattern analysis
  • Time gap detection
  • Seasonal trend monitoring
  • Data freshness tracking
Typical use cases:
  • Event timestamps
  • Creation/modification dates
  • Transaction times
  • Log timestamps
  • Scheduled events
Supported formats:
  • Unix timestamps
  • ISO 8601 strings
  • Pandas datetime objects
  • Various date formats (with parsing)

​
VECTOR = ‘vector’

Multi-dimensional numerical vectors (embeddings). Used for embedding vectors, feature vectors, and other multi-dimensional numerical data. Enables embedding-based drift detection and clustering analysis. Characteristics:
  • Fixed-dimension numerical arrays
  • Embedding-based analysis
  • Vector similarity metrics
  • Clustering support
Monitoring features:
  • Embedding drift detection
  • Cluster analysis and visualization
  • Vector similarity tracking
  • Dimensionality validation
Typical use cases:
  • Text embeddings (Word2Vec, BERT, etc.)
  • Image embeddings (CNN features)
  • User/item embeddings
  • Feature vectors from neural networks
  • Recommendation system embeddings
Special considerations:
  • Requires consistent vector dimensions
  • Benefits from custom feature definitions
  • Supports clustering and UMAP visualization

​
is_numeric()

Check if the data type is numeric.

​
Returns

True if data type is INTEGER or FLOAT Return type: bool

​
is_bool_or_cat()

Check if the data type is boolean or categorical.

​
Returns

True if data type is BOOLEAN or CATEGORY Return type: bool

​
is_vector()

Check if the data type is vector.

​
Returns

True if data type is VECTOR Return type: bool