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Viewing docs for Databricks v1.109.0
published on Tuesday, Sep 8, 2026 by Pulumi
Schema (JSON)
pulumi/pulumi-databricks

databricks.getFeatureEngineeringFeature

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Viewing docs for Databricks v1.109.0
published on Tuesday, Sep 8, 2026 by Pulumi
Schema (JSON)
pulumi/pulumi-databricks

    Private Preview

    Using getFeatureEngineeringFeature

    Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

    function getFeatureEngineeringFeature(args: GetFeatureEngineeringFeatureArgs, opts?: InvokeOptions): Promise<GetFeatureEngineeringFeatureResult>
function getFeatureEngineeringFeatureOutput(args: GetFeatureEngineeringFeatureOutputArgs, opts?: InvokeOutputOptions): Output<GetFeatureEngineeringFeatureResult>
    def get_feature_engineering_feature(full_name: Optional[str] = None,
                                    provider_config: Optional[GetFeatureEngineeringFeatureProviderConfig] = None,
                                    opts: Optional[InvokeOptions] = None) -> GetFeatureEngineeringFeatureResult
def get_feature_engineering_feature_output(full_name: pulumi.Input[Optional[str]] = None,
                                    provider_config: pulumi.Input[Optional[GetFeatureEngineeringFeatureProviderConfigArgs]] = None,
                                    opts: Optional[InvokeOutputOptions] = None) -> Output[GetFeatureEngineeringFeatureResult]
    func LookupFeatureEngineeringFeature(ctx *Context, args *LookupFeatureEngineeringFeatureArgs, opts ...InvokeOption) (*LookupFeatureEngineeringFeatureResult, error)
func LookupFeatureEngineeringFeatureOutput(ctx *Context, args *LookupFeatureEngineeringFeatureOutputArgs, opts ...InvokeOption) LookupFeatureEngineeringFeatureResultOutput

    > Note: This function is named LookupFeatureEngineeringFeature in the Go SDK.

    public static class GetFeatureEngineeringFeature 
{
    public static Task<GetFeatureEngineeringFeatureResult> InvokeAsync(GetFeatureEngineeringFeatureArgs args, InvokeOptions? opts = null)
    public static Output<GetFeatureEngineeringFeatureResult> Invoke(GetFeatureEngineeringFeatureInvokeArgs args, InvokeOptions? opts = null)
    public static Output<GetFeatureEngineeringFeatureResult> Invoke(GetFeatureEngineeringFeatureInvokeArgs args, InvokeOutputOptions opts)
}
    public static CompletableFuture<GetFeatureEngineeringFeatureResult> getFeatureEngineeringFeature(GetFeatureEngineeringFeatureArgs args, InvokeOptions options)
public static Output<GetFeatureEngineeringFeatureResult> getFeatureEngineeringFeature(GetFeatureEngineeringFeatureArgs args, InvokeOptions options)
public static Output<GetFeatureEngineeringFeatureResult> getFeatureEngineeringFeature(GetFeatureEngineeringFeatureArgs args, InvokeOutputOptions options)

    fn::invoke:
  function: databricks:index/getFeatureEngineeringFeature:getFeatureEngineeringFeature
  arguments:
    # arguments dictionary
    data "databricks_get_feature_engineering_feature" "name" {
    # arguments
}

    The following arguments are supported:

    FullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    ProviderConfig GetFeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    FullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    ProviderConfig GetFeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    full_name string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    provider_config object
    Configure the provider for management through account provider.
    fullName String
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    providerConfig GetFeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    fullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    providerConfig GetFeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    full_name str
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    provider_config GetFeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    fullName String
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    providerConfig Property Map
    Configure the provider for management through account provider.

    getFeatureEngineeringFeature Result

    The following output properties are available:

    CatalogName string
    (string) - Name of parent catalog
    CreatedAt string
    (string) - Time at which this feature was created
    CreatedBy string
    (string) - Username of the feature creator
    Description string
    (string) - The description of the feature
    Entities List<GetFeatureEngineeringFeatureEntity>
    (list of EntityColumn) - The entity columns for the feature, used as aggregation keys and for query-time lookup
    FullName string
    (string) - Three-part full name of the Stream (catalog.schema.stream)
    Function GetFeatureEngineeringFeatureFunction
    (Function) - The function by which the feature is computed
    LineageContext GetFeatureEngineeringFeatureLineageContext
    (LineageContext) - Lineage context information for this feature. WARNING: This field is primarily intended for internal use by Databricks systems and is automatically populated when features are created through Databricks notebooks or jobs. Users should not manually set this field as incorrect values may lead to inaccurate lineage tracking or unexpected behavior. This field will be set by feature-engineering client and should be left unset by SDK and terraform users
    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    SchemaName string
    (string) - Name of parent schema relative to its parent catalog
    Source GetFeatureEngineeringFeatureSource
    (DataSource) - The data source of the feature
    TimeseriesColumn GetFeatureEngineeringFeatureTimeseriesColumn
    (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
    ProviderConfig GetFeatureEngineeringFeatureProviderConfig
    CatalogName string
    (string) - Name of parent catalog
    CreatedAt string
    (string) - Time at which this feature was created
    CreatedBy string
    (string) - Username of the feature creator
    Description string
    (string) - The description of the feature
    Entities []GetFeatureEngineeringFeatureEntity
    (list of EntityColumn) - The entity columns for the feature, used as aggregation keys and for query-time lookup
    FullName string
    (string) - Three-part full name of the Stream (catalog.schema.stream)
    Function GetFeatureEngineeringFeatureFunction
    (Function) - The function by which the feature is computed
    LineageContext GetFeatureEngineeringFeatureLineageContext
    (LineageContext) - Lineage context information for this feature. WARNING: This field is primarily intended for internal use by Databricks systems and is automatically populated when features are created through Databricks notebooks or jobs. Users should not manually set this field as incorrect values may lead to inaccurate lineage tracking or unexpected behavior. This field will be set by feature-engineering client and should be left unset by SDK and terraform users
    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    SchemaName string
    (string) - Name of parent schema relative to its parent catalog
    Source GetFeatureEngineeringFeatureSource
    (DataSource) - The data source of the feature
    TimeseriesColumn GetFeatureEngineeringFeatureTimeseriesColumn
    (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
    ProviderConfig GetFeatureEngineeringFeatureProviderConfig
    catalog_name string
    (string) - Name of parent catalog
    created_at string
    (string) - Time at which this feature was created
    created_by string
    (string) - Username of the feature creator
    description string
    (string) - The description of the feature
    entities list(object)
    (list of EntityColumn) - The entity columns for the feature, used as aggregation keys and for query-time lookup
    full_name string
    (string) - Three-part full name of the Stream (catalog.schema.stream)
    function object
    (Function) - The function by which the feature is computed
    lineage_context object
    (LineageContext) - Lineage context information for this feature. WARNING: This field is primarily intended for internal use by Databricks systems and is automatically populated when features are created through Databricks notebooks or jobs. Users should not manually set this field as incorrect values may lead to inaccurate lineage tracking or unexpected behavior. This field will be set by feature-engineering client and should be left unset by SDK and terraform users
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    schema_name string
    (string) - Name of parent schema relative to its parent catalog
    source object
    (DataSource) - The data source of the feature
    timeseries_column object
    (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
    provider_config object
    catalogName String
    (string) - Name of parent catalog
    createdAt String
    (string) - Time at which this feature was created
    createdBy String
    (string) - Username of the feature creator
    description String
    (string) - The description of the feature
    entities List<GetFeatureEngineeringFeatureEntity>
    (list of EntityColumn) - The entity columns for the feature, used as aggregation keys and for query-time lookup
    fullName String
    (string) - Three-part full name of the Stream (catalog.schema.stream)
    function GetFeatureEngineeringFeatureFunction
    (Function) - The function by which the feature is computed
    lineageContext GetFeatureEngineeringFeatureLineageContext
    (LineageContext) - Lineage context information for this feature. WARNING: This field is primarily intended for internal use by Databricks systems and is automatically populated when features are created through Databricks notebooks or jobs. Users should not manually set this field as incorrect values may lead to inaccurate lineage tracking or unexpected behavior. This field will be set by feature-engineering client and should be left unset by SDK and terraform users
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    schemaName String
    (string) - Name of parent schema relative to its parent catalog
    source GetFeatureEngineeringFeatureSource
    (DataSource) - The data source of the feature
    timeseriesColumn GetFeatureEngineeringFeatureTimeseriesColumn
    (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
    providerConfig GetFeatureEngineeringFeatureProviderConfig
    catalogName string
    (string) - Name of parent catalog
    createdAt string
    (string) - Time at which this feature was created
    createdBy string
    (string) - Username of the feature creator
    description string
    (string) - The description of the feature
    entities GetFeatureEngineeringFeatureEntity[]
    (list of EntityColumn) - The entity columns for the feature, used as aggregation keys and for query-time lookup
    fullName string
    (string) - Three-part full name of the Stream (catalog.schema.stream)
    function GetFeatureEngineeringFeatureFunction
    (Function) - The function by which the feature is computed
    lineageContext GetFeatureEngineeringFeatureLineageContext
    (LineageContext) - Lineage context information for this feature. WARNING: This field is primarily intended for internal use by Databricks systems and is automatically populated when features are created through Databricks notebooks or jobs. Users should not manually set this field as incorrect values may lead to inaccurate lineage tracking or unexpected behavior. This field will be set by feature-engineering client and should be left unset by SDK and terraform users
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    schemaName string
    (string) - Name of parent schema relative to its parent catalog
    source GetFeatureEngineeringFeatureSource
    (DataSource) - The data source of the feature
    timeseriesColumn GetFeatureEngineeringFeatureTimeseriesColumn
    (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
    providerConfig GetFeatureEngineeringFeatureProviderConfig
    catalog_name str
    (string) - Name of parent catalog
    created_at str
    (string) - Time at which this feature was created
    created_by str
    (string) - Username of the feature creator
    description str
    (string) - The description of the feature
    entities Sequence[GetFeatureEngineeringFeatureEntity]
    (list of EntityColumn) - The entity columns for the feature, used as aggregation keys and for query-time lookup
    full_name str
    (string) - Three-part full name of the Stream (catalog.schema.stream)
    function GetFeatureEngineeringFeatureFunction
    (Function) - The function by which the feature is computed
    lineage_context GetFeatureEngineeringFeatureLineageContext
    (LineageContext) - Lineage context information for this feature. WARNING: This field is primarily intended for internal use by Databricks systems and is automatically populated when features are created through Databricks notebooks or jobs. Users should not manually set this field as incorrect values may lead to inaccurate lineage tracking or unexpected behavior. This field will be set by feature-engineering client and should be left unset by SDK and terraform users
    name str
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    schema_name str
    (string) - Name of parent schema relative to its parent catalog
    source GetFeatureEngineeringFeatureSource
    (DataSource) - The data source of the feature
    timeseries_column GetFeatureEngineeringFeatureTimeseriesColumn
    (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
    provider_config GetFeatureEngineeringFeatureProviderConfig
    catalogName String
    (string) - Name of parent catalog
    createdAt String
    (string) - Time at which this feature was created
    createdBy String
    (string) - Username of the feature creator
    description String
    (string) - The description of the feature
    entities List<Property Map>
    (list of EntityColumn) - The entity columns for the feature, used as aggregation keys and for query-time lookup
    fullName String
    (string) - Three-part full name of the Stream (catalog.schema.stream)
    function Property Map
    (Function) - The function by which the feature is computed
    lineageContext Property Map
    (LineageContext) - Lineage context information for this feature. WARNING: This field is primarily intended for internal use by Databricks systems and is automatically populated when features are created through Databricks notebooks or jobs. Users should not manually set this field as incorrect values may lead to inaccurate lineage tracking or unexpected behavior. This field will be set by feature-engineering client and should be left unset by SDK and terraform users
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    schemaName String
    (string) - Name of parent schema relative to its parent catalog
    source Property Map
    (DataSource) - The data source of the feature
    timeseriesColumn Property Map
    (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
    providerConfig Property Map

    Supporting Types

    GetFeatureEngineeringFeatureEntity

    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name str
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation

    GetFeatureEngineeringFeatureFunction

    AggregationFunction GetFeatureEngineeringFeatureFunctionAggregationFunction
    (AggregationFunction) - An aggregation function applied over a time window
    ColumnSelection GetFeatureEngineeringFeatureFunctionColumnSelection
    (ColumnSelection) - Selects the latest value of a single column in a data source
    CustomUdf GetFeatureEngineeringFeatureFunctionCustomUdf
    (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns
    AggregationFunction GetFeatureEngineeringFeatureFunctionAggregationFunction
    (AggregationFunction) - An aggregation function applied over a time window
    ColumnSelection GetFeatureEngineeringFeatureFunctionColumnSelection
    (ColumnSelection) - Selects the latest value of a single column in a data source
    CustomUdf GetFeatureEngineeringFeatureFunctionCustomUdf
    (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns
    aggregation_function object
    (AggregationFunction) - An aggregation function applied over a time window
    column_selection object
    (ColumnSelection) - Selects the latest value of a single column in a data source
    custom_udf object
    (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns
    aggregationFunction GetFeatureEngineeringFeatureFunctionAggregationFunction
    (AggregationFunction) - An aggregation function applied over a time window
    columnSelection GetFeatureEngineeringFeatureFunctionColumnSelection
    (ColumnSelection) - Selects the latest value of a single column in a data source
    customUdf GetFeatureEngineeringFeatureFunctionCustomUdf
    (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns
    aggregationFunction GetFeatureEngineeringFeatureFunctionAggregationFunction
    (AggregationFunction) - An aggregation function applied over a time window
    columnSelection GetFeatureEngineeringFeatureFunctionColumnSelection
    (ColumnSelection) - Selects the latest value of a single column in a data source
    customUdf GetFeatureEngineeringFeatureFunctionCustomUdf
    (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns
    aggregation_function GetFeatureEngineeringFeatureFunctionAggregationFunction
    (AggregationFunction) - An aggregation function applied over a time window
    column_selection GetFeatureEngineeringFeatureFunctionColumnSelection
    (ColumnSelection) - Selects the latest value of a single column in a data source
    custom_udf GetFeatureEngineeringFeatureFunctionCustomUdf
    (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns
    aggregationFunction Property Map
    (AggregationFunction) - An aggregation function applied over a time window
    columnSelection Property Map
    (ColumnSelection) - Selects the latest value of a single column in a data source
    customUdf Property Map
    (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns

    GetFeatureEngineeringFeatureFunctionAggregationFunction

    ApproxCountDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    (ApproxCountDistinctFunction)
    ApproxPercentile GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    (ApproxPercentileFunction)
    Avg GetFeatureEngineeringFeatureFunctionAggregationFunctionAvg
    (AvgFunction)
    CountFunction GetFeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    (CountFunction)
    First GetFeatureEngineeringFeatureFunctionAggregationFunctionFirst
    (FirstFunction)
    FirstDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    (FirstDistinctFunction)
    FirstN GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    (FirstNFunction)
    Last GetFeatureEngineeringFeatureFunctionAggregationFunctionLast
    (LastFunction)
    LastDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    (LastDistinctFunction)
    LastN GetFeatureEngineeringFeatureFunctionAggregationFunctionLastN
    (LastNFunction)
    Max GetFeatureEngineeringFeatureFunctionAggregationFunctionMax
    (MaxFunction)
    Min GetFeatureEngineeringFeatureFunctionAggregationFunctionMin
    (MinFunction)
    StddevPop GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    (StddevPopFunction)
    StddevSamp GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    (StddevSampFunction)
    Sum GetFeatureEngineeringFeatureFunctionAggregationFunctionSum
    (SumFunction)
    TimeWindow GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    (TimeWindow) - The time window over which the aggregation is computed
    VarPop GetFeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    (VarPopFunction)
    VarSamp GetFeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    (VarSampFunction)
    ApproxCountDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    (ApproxCountDistinctFunction)
    ApproxPercentile GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    (ApproxPercentileFunction)
    Avg GetFeatureEngineeringFeatureFunctionAggregationFunctionAvg
    (AvgFunction)
    CountFunction GetFeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    (CountFunction)
    First GetFeatureEngineeringFeatureFunctionAggregationFunctionFirst
    (FirstFunction)
    FirstDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    (FirstDistinctFunction)
    FirstN GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    (FirstNFunction)
    Last GetFeatureEngineeringFeatureFunctionAggregationFunctionLast
    (LastFunction)
    LastDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    (LastDistinctFunction)
    LastN GetFeatureEngineeringFeatureFunctionAggregationFunctionLastN
    (LastNFunction)
    Max GetFeatureEngineeringFeatureFunctionAggregationFunctionMax
    (MaxFunction)
    Min GetFeatureEngineeringFeatureFunctionAggregationFunctionMin
    (MinFunction)
    StddevPop GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    (StddevPopFunction)
    StddevSamp GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    (StddevSampFunction)
    Sum GetFeatureEngineeringFeatureFunctionAggregationFunctionSum
    (SumFunction)
    TimeWindow GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    (TimeWindow) - The time window over which the aggregation is computed
    VarPop GetFeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    (VarPopFunction)
    VarSamp GetFeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    (VarSampFunction)
    approx_count_distinct object
    (ApproxCountDistinctFunction)
    approx_percentile object
    (ApproxPercentileFunction)
    avg object
    (AvgFunction)
    count_function object
    (CountFunction)
    first object
    (FirstFunction)
    first_distinct object
    (FirstDistinctFunction)
    first_n object
    (FirstNFunction)
    last object
    (LastFunction)
    last_distinct object
    (LastDistinctFunction)
    last_n object
    (LastNFunction)
    max object
    (MaxFunction)
    min object
    (MinFunction)
    stddev_pop object
    (StddevPopFunction)
    stddev_samp object
    (StddevSampFunction)
    sum object
    (SumFunction)
    time_window object
    (TimeWindow) - The time window over which the aggregation is computed
    var_pop object
    (VarPopFunction)
    var_samp object
    (VarSampFunction)
    approxCountDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    (ApproxCountDistinctFunction)
    approxPercentile GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    (ApproxPercentileFunction)
    avg GetFeatureEngineeringFeatureFunctionAggregationFunctionAvg
    (AvgFunction)
    countFunction GetFeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    (CountFunction)
    first GetFeatureEngineeringFeatureFunctionAggregationFunctionFirst
    (FirstFunction)
    firstDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    (FirstDistinctFunction)
    firstN GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    (FirstNFunction)
    last GetFeatureEngineeringFeatureFunctionAggregationFunctionLast
    (LastFunction)
    lastDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    (LastDistinctFunction)
    lastN GetFeatureEngineeringFeatureFunctionAggregationFunctionLastN
    (LastNFunction)
    max GetFeatureEngineeringFeatureFunctionAggregationFunctionMax
    (MaxFunction)
    min GetFeatureEngineeringFeatureFunctionAggregationFunctionMin
    (MinFunction)
    stddevPop GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    (StddevPopFunction)
    stddevSamp GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    (StddevSampFunction)
    sum GetFeatureEngineeringFeatureFunctionAggregationFunctionSum
    (SumFunction)
    timeWindow GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    (TimeWindow) - The time window over which the aggregation is computed
    varPop GetFeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    (VarPopFunction)
    varSamp GetFeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    (VarSampFunction)
    approxCountDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    (ApproxCountDistinctFunction)
    approxPercentile GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    (ApproxPercentileFunction)
    avg GetFeatureEngineeringFeatureFunctionAggregationFunctionAvg
    (AvgFunction)
    countFunction GetFeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    (CountFunction)
    first GetFeatureEngineeringFeatureFunctionAggregationFunctionFirst
    (FirstFunction)
    firstDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    (FirstDistinctFunction)
    firstN GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    (FirstNFunction)
    last GetFeatureEngineeringFeatureFunctionAggregationFunctionLast
    (LastFunction)
    lastDistinct GetFeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    (LastDistinctFunction)
    lastN GetFeatureEngineeringFeatureFunctionAggregationFunctionLastN
    (LastNFunction)
    max GetFeatureEngineeringFeatureFunctionAggregationFunctionMax
    (MaxFunction)
    min GetFeatureEngineeringFeatureFunctionAggregationFunctionMin
    (MinFunction)
    stddevPop GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    (StddevPopFunction)
    stddevSamp GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    (StddevSampFunction)
    sum GetFeatureEngineeringFeatureFunctionAggregationFunctionSum
    (SumFunction)
    timeWindow GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    (TimeWindow) - The time window over which the aggregation is computed
    varPop GetFeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    (VarPopFunction)
    varSamp GetFeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    (VarSampFunction)
    approx_count_distinct GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    (ApproxCountDistinctFunction)
    approx_percentile GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    (ApproxPercentileFunction)
    avg GetFeatureEngineeringFeatureFunctionAggregationFunctionAvg
    (AvgFunction)
    count_function GetFeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    (CountFunction)
    first GetFeatureEngineeringFeatureFunctionAggregationFunctionFirst
    (FirstFunction)
    first_distinct GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    (FirstDistinctFunction)
    first_n GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    (FirstNFunction)
    last GetFeatureEngineeringFeatureFunctionAggregationFunctionLast
    (LastFunction)
    last_distinct GetFeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    (LastDistinctFunction)
    last_n GetFeatureEngineeringFeatureFunctionAggregationFunctionLastN
    (LastNFunction)
    max GetFeatureEngineeringFeatureFunctionAggregationFunctionMax
    (MaxFunction)
    min GetFeatureEngineeringFeatureFunctionAggregationFunctionMin
    (MinFunction)
    stddev_pop GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    (StddevPopFunction)
    stddev_samp GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    (StddevSampFunction)
    sum GetFeatureEngineeringFeatureFunctionAggregationFunctionSum
    (SumFunction)
    time_window GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    (TimeWindow) - The time window over which the aggregation is computed
    var_pop GetFeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    (VarPopFunction)
    var_samp GetFeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    (VarSampFunction)
    approxCountDistinct Property Map
    (ApproxCountDistinctFunction)
    approxPercentile Property Map
    (ApproxPercentileFunction)
    avg Property Map
    (AvgFunction)
    countFunction Property Map
    (CountFunction)
    first Property Map
    (FirstFunction)
    firstDistinct Property Map
    (FirstDistinctFunction)
    firstN Property Map
    (FirstNFunction)
    last Property Map
    (LastFunction)
    lastDistinct Property Map
    (LastDistinctFunction)
    lastN Property Map
    (LastNFunction)
    max Property Map
    (MaxFunction)
    min Property Map
    (MinFunction)
    stddevPop Property Map
    (StddevPopFunction)
    stddevSamp Property Map
    (StddevSampFunction)
    sum Property Map
    (SumFunction)
    timeWindow Property Map
    (TimeWindow) - The time window over which the aggregation is computed
    varPop Property Map
    (VarPopFunction)
    varSamp Property Map
    (VarSampFunction)

    GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct

    Input string
    (string) - The input column from which the sample variance is computed
    RelativeSd double
    (number) - The maximum relative standard deviation allowed (default defined by Spark)
    Input string
    (string) - The input column from which the sample variance is computed
    RelativeSd float64
    (number) - The maximum relative standard deviation allowed (default defined by Spark)
    input string
    (string) - The input column from which the sample variance is computed
    relative_sd number
    (number) - The maximum relative standard deviation allowed (default defined by Spark)
    input String
    (string) - The input column from which the sample variance is computed
    relativeSd Double
    (number) - The maximum relative standard deviation allowed (default defined by Spark)
    input string
    (string) - The input column from which the sample variance is computed
    relativeSd number
    (number) - The maximum relative standard deviation allowed (default defined by Spark)
    input str
    (string) - The input column from which the sample variance is computed
    relative_sd float
    (number) - The maximum relative standard deviation allowed (default defined by Spark)
    input String
    (string) - The input column from which the sample variance is computed
    relativeSd Number
    (number) - The maximum relative standard deviation allowed (default defined by Spark)

    GetFeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile

    Input string
    (string) - The input column from which the sample variance is computed
    Percentile double
    (number) - The percentile value to compute (between 0 and 1)
    Accuracy int
    (integer) - The accuracy parameter (higher is more accurate but slower)
    Input string
    (string) - The input column from which the sample variance is computed
    Percentile float64
    (number) - The percentile value to compute (between 0 and 1)
    Accuracy int
    (integer) - The accuracy parameter (higher is more accurate but slower)
    input string
    (string) - The input column from which the sample variance is computed
    percentile number
    (number) - The percentile value to compute (between 0 and 1)
    accuracy number
    (integer) - The accuracy parameter (higher is more accurate but slower)
    input String
    (string) - The input column from which the sample variance is computed
    percentile Double
    (number) - The percentile value to compute (between 0 and 1)
    accuracy Integer
    (integer) - The accuracy parameter (higher is more accurate but slower)
    input string
    (string) - The input column from which the sample variance is computed
    percentile number
    (number) - The percentile value to compute (between 0 and 1)
    accuracy number
    (integer) - The accuracy parameter (higher is more accurate but slower)
    input str
    (string) - The input column from which the sample variance is computed
    percentile float
    (number) - The percentile value to compute (between 0 and 1)
    accuracy int
    (integer) - The accuracy parameter (higher is more accurate but slower)
    input String
    (string) - The input column from which the sample variance is computed
    percentile Number
    (number) - The percentile value to compute (between 0 and 1)
    accuracy Number
    (integer) - The accuracy parameter (higher is more accurate but slower)

    GetFeatureEngineeringFeatureFunctionAggregationFunctionAvg

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionCountFunction

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionFirst

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct

    Input string
    (string) - The input column from which the sample variance is computed
    N int
    (integer) - The number of values to return
    Input string
    (string) - The input column from which the sample variance is computed
    N int
    (integer) - The number of values to return
    input string
    (string) - The input column from which the sample variance is computed
    n number
    (integer) - The number of values to return
    input String
    (string) - The input column from which the sample variance is computed
    n Integer
    (integer) - The number of values to return
    input string
    (string) - The input column from which the sample variance is computed
    n number
    (integer) - The number of values to return
    input str
    (string) - The input column from which the sample variance is computed
    n int
    (integer) - The number of values to return
    input String
    (string) - The input column from which the sample variance is computed
    n Number
    (integer) - The number of values to return

    GetFeatureEngineeringFeatureFunctionAggregationFunctionFirstN

    Input string
    (string) - The input column from which the sample variance is computed
    N int
    (integer) - The number of values to return
    Input string
    (string) - The input column from which the sample variance is computed
    N int
    (integer) - The number of values to return
    input string
    (string) - The input column from which the sample variance is computed
    n number
    (integer) - The number of values to return
    input String
    (string) - The input column from which the sample variance is computed
    n Integer
    (integer) - The number of values to return
    input string
    (string) - The input column from which the sample variance is computed
    n number
    (integer) - The number of values to return
    input str
    (string) - The input column from which the sample variance is computed
    n int
    (integer) - The number of values to return
    input String
    (string) - The input column from which the sample variance is computed
    n Number
    (integer) - The number of values to return

    GetFeatureEngineeringFeatureFunctionAggregationFunctionLast

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct

    Input string
    (string) - The input column from which the sample variance is computed
    N int
    (integer) - The number of values to return
    Input string
    (string) - The input column from which the sample variance is computed
    N int
    (integer) - The number of values to return
    input string
    (string) - The input column from which the sample variance is computed
    n number
    (integer) - The number of values to return
    input String
    (string) - The input column from which the sample variance is computed
    n Integer
    (integer) - The number of values to return
    input string
    (string) - The input column from which the sample variance is computed
    n number
    (integer) - The number of values to return
    input str
    (string) - The input column from which the sample variance is computed
    n int
    (integer) - The number of values to return
    input String
    (string) - The input column from which the sample variance is computed
    n Number
    (integer) - The number of values to return

    GetFeatureEngineeringFeatureFunctionAggregationFunctionLastN

    Input string
    (string) - The input column from which the sample variance is computed
    N int
    (integer) - The number of values to return
    Input string
    (string) - The input column from which the sample variance is computed
    N int
    (integer) - The number of values to return
    input string
    (string) - The input column from which the sample variance is computed
    n number
    (integer) - The number of values to return
    input String
    (string) - The input column from which the sample variance is computed
    n Integer
    (integer) - The number of values to return
    input string
    (string) - The input column from which the sample variance is computed
    n number
    (integer) - The number of values to return
    input str
    (string) - The input column from which the sample variance is computed
    n int
    (integer) - The number of values to return
    input String
    (string) - The input column from which the sample variance is computed
    n Number
    (integer) - The number of values to return

    GetFeatureEngineeringFeatureFunctionAggregationFunctionMax

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionMin

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevPop

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionSum

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow

    Rolling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    (RollingWindow)
    Sawtooth GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
    Sliding GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    (SlidingWindow)
    StartTime string
    (string) - Earliest event-time boundary at which the Feature may emit an output. This gates outputs, not the historical inputs read by a window. For example, a 365-day window with start_time=2026-01-01 begins emitting partial-window values on that date instead of waiting for 365 days of data; a lifetime window produces no output before start_time. If unset, tumbling and fixed-duration sliding windows first emit at an offset-aligned boundary after a full window can be formed. If unset, lifetime sliding windows and rolling windows emit as soon as eligible source data exists
    Tumbling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    (TumblingWindow)
    Rolling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    (RollingWindow)
    Sawtooth GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
    Sliding GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    (SlidingWindow)
    StartTime string
    (string) - Earliest event-time boundary at which the Feature may emit an output. This gates outputs, not the historical inputs read by a window. For example, a 365-day window with start_time=2026-01-01 begins emitting partial-window values on that date instead of waiting for 365 days of data; a lifetime window produces no output before start_time. If unset, tumbling and fixed-duration sliding windows first emit at an offset-aligned boundary after a full window can be formed. If unset, lifetime sliding windows and rolling windows emit as soon as eligible source data exists
    Tumbling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    (TumblingWindow)
    rolling object
    (RollingWindow)
    sawtooth object
    (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
    sliding object
    (SlidingWindow)
    start_time string
    (string) - Earliest event-time boundary at which the Feature may emit an output. This gates outputs, not the historical inputs read by a window. For example, a 365-day window with start_time=2026-01-01 begins emitting partial-window values on that date instead of waiting for 365 days of data; a lifetime window produces no output before start_time. If unset, tumbling and fixed-duration sliding windows first emit at an offset-aligned boundary after a full window can be formed. If unset, lifetime sliding windows and rolling windows emit as soon as eligible source data exists
    tumbling object
    (TumblingWindow)
    rolling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    (RollingWindow)
    sawtooth GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
    sliding GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    (SlidingWindow)
    startTime String
    (string) - Earliest event-time boundary at which the Feature may emit an output. This gates outputs, not the historical inputs read by a window. For example, a 365-day window with start_time=2026-01-01 begins emitting partial-window values on that date instead of waiting for 365 days of data; a lifetime window produces no output before start_time. If unset, tumbling and fixed-duration sliding windows first emit at an offset-aligned boundary after a full window can be formed. If unset, lifetime sliding windows and rolling windows emit as soon as eligible source data exists
    tumbling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    (TumblingWindow)
    rolling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    (RollingWindow)
    sawtooth GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
    sliding GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    (SlidingWindow)
    startTime string
    (string) - Earliest event-time boundary at which the Feature may emit an output. This gates outputs, not the historical inputs read by a window. For example, a 365-day window with start_time=2026-01-01 begins emitting partial-window values on that date instead of waiting for 365 days of data; a lifetime window produces no output before start_time. If unset, tumbling and fixed-duration sliding windows first emit at an offset-aligned boundary after a full window can be formed. If unset, lifetime sliding windows and rolling windows emit as soon as eligible source data exists
    tumbling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    (TumblingWindow)
    rolling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    (RollingWindow)
    sawtooth GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
    sliding GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    (SlidingWindow)
    start_time str
    (string) - Earliest event-time boundary at which the Feature may emit an output. This gates outputs, not the historical inputs read by a window. For example, a 365-day window with start_time=2026-01-01 begins emitting partial-window values on that date instead of waiting for 365 days of data; a lifetime window produces no output before start_time. If unset, tumbling and fixed-duration sliding windows first emit at an offset-aligned boundary after a full window can be formed. If unset, lifetime sliding windows and rolling windows emit as soon as eligible source data exists
    tumbling GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    (TumblingWindow)
    rolling Property Map
    (RollingWindow)
    sawtooth Property Map
    (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
    sliding Property Map
    (SlidingWindow)
    startTime String
    (string) - Earliest event-time boundary at which the Feature may emit an output. This gates outputs, not the historical inputs read by a window. For example, a 365-day window with start_time=2026-01-01 begins emitting partial-window values on that date instead of waiting for 365 days of data; a lifetime window produces no output before start_time. If unset, tumbling and fixed-duration sliding windows first emit at an offset-aligned boundary after a full window can be formed. If unset, lifetime sliding windows and rolling windows emit as soon as eligible source data exists
    tumbling Property Map
    (TumblingWindow)

    GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling

    Delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    WindowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    Delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    WindowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    window_duration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay String
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    windowDuration String
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    windowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay str
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    window_duration str
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay String
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    windowDuration String
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)

    GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth

    Delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    WindowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    Delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    WindowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    window_duration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay String
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    windowDuration String
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    windowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay str
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    window_duration str
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay String
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    windowDuration String
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)

    GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding

    SlideDuration string
    (string) - The slide duration (interval by which windows advance, must be positive and less than duration)
    Delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    Offset string
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    WindowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    SlideDuration string
    (string) - The slide duration (interval by which windows advance, must be positive and less than duration)
    Delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    Offset string
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    WindowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    slide_duration string
    (string) - The slide duration (interval by which windows advance, must be positive and less than duration)
    delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset string
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    window_duration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    slideDuration String
    (string) - The slide duration (interval by which windows advance, must be positive and less than duration)
    delay String
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset String
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    windowDuration String
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    slideDuration string
    (string) - The slide duration (interval by which windows advance, must be positive and less than duration)
    delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset string
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    windowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    slide_duration str
    (string) - The slide duration (interval by which windows advance, must be positive and less than duration)
    delay str
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset str
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    window_duration str
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    slideDuration String
    (string) - The slide duration (interval by which windows advance, must be positive and less than duration)
    delay String
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset String
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    windowDuration String
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)

    GetFeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling

    WindowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    Delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    Offset string
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    WindowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    Delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    Offset string
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    window_duration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset string
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    windowDuration String
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay String
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset String
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    windowDuration string
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay string
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset string
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    window_duration str
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay str
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset str
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration
    windowDuration String
    (string) - The duration of each tumbling window (non-overlapping, fixed-duration windows)
    delay String
    (string) - Non-negative analytic lag that evaluates the window this far in the past. Use this for timing variations unrelated to source lateness, such as a 30-day count as of one week ago. If unset, the analytic lag is zero. It composes with source.lateness when both are set
    offset String
    (string) - Non-negative phase shift from the default midnight UTC alignment. For example, offset=22h on a 24h window produces boundaries at 22:00 UTC (17:00 New York in standard time) instead of midnight UTC. If unset, the offset is zero. Must be shorter than window_duration

    GetFeatureEngineeringFeatureFunctionAggregationFunctionVarPop

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionAggregationFunctionVarSamp

    Input string
    (string) - The input column from which the sample variance is computed
    Input string
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed
    input string
    (string) - The input column from which the sample variance is computed
    input str
    (string) - The input column from which the sample variance is computed
    input String
    (string) - The input column from which the sample variance is computed

    GetFeatureEngineeringFeatureFunctionColumnSelection

    Column string
    (string) - Source column whose value is passed for this parameter at execution time
    Column string
    (string) - Source column whose value is passed for this parameter at execution time
    column string
    (string) - Source column whose value is passed for this parameter at execution time
    column String
    (string) - Source column whose value is passed for this parameter at execution time
    column string
    (string) - Source column whose value is passed for this parameter at execution time
    column str
    (string) - Source column whose value is passed for this parameter at execution time
    column String
    (string) - Source column whose value is passed for this parameter at execution time

    GetFeatureEngineeringFeatureFunctionCustomUdf

    FunctionPath string
    (string) - Fully qualified 3-part Unity Catalog path of the function to apply
    InputBindings List<GetFeatureEngineeringFeatureFunctionCustomUdfInputBinding>
    (list of InputBinding) - Binds each UC function parameter to a source column. May be empty for zero-argument functions (e.g. a timestamp generator)
    FunctionPath string
    (string) - Fully qualified 3-part Unity Catalog path of the function to apply
    InputBindings []GetFeatureEngineeringFeatureFunctionCustomUdfInputBinding
    (list of InputBinding) - Binds each UC function parameter to a source column. May be empty for zero-argument functions (e.g. a timestamp generator)
    function_path string
    (string) - Fully qualified 3-part Unity Catalog path of the function to apply
    input_bindings list(object)
    (list of InputBinding) - Binds each UC function parameter to a source column. May be empty for zero-argument functions (e.g. a timestamp generator)
    functionPath String
    (string) - Fully qualified 3-part Unity Catalog path of the function to apply
    inputBindings List<GetFeatureEngineeringFeatureFunctionCustomUdfInputBinding>
    (list of InputBinding) - Binds each UC function parameter to a source column. May be empty for zero-argument functions (e.g. a timestamp generator)
    functionPath string
    (string) - Fully qualified 3-part Unity Catalog path of the function to apply
    inputBindings GetFeatureEngineeringFeatureFunctionCustomUdfInputBinding[]
    (list of InputBinding) - Binds each UC function parameter to a source column. May be empty for zero-argument functions (e.g. a timestamp generator)
    function_path str
    (string) - Fully qualified 3-part Unity Catalog path of the function to apply
    input_bindings Sequence[GetFeatureEngineeringFeatureFunctionCustomUdfInputBinding]
    (list of InputBinding) - Binds each UC function parameter to a source column. May be empty for zero-argument functions (e.g. a timestamp generator)
    functionPath String
    (string) - Fully qualified 3-part Unity Catalog path of the function to apply
    inputBindings List<Property Map>
    (list of InputBinding) - Binds each UC function parameter to a source column. May be empty for zero-argument functions (e.g. a timestamp generator)

    GetFeatureEngineeringFeatureFunctionCustomUdfInputBinding

    Column string
    (string) - Source column whose value is passed for this parameter at execution time
    Parameter string
    (string) - Name of the UC function parameter
    Column string
    (string) - Source column whose value is passed for this parameter at execution time
    Parameter string
    (string) - Name of the UC function parameter
    column string
    (string) - Source column whose value is passed for this parameter at execution time
    parameter string
    (string) - Name of the UC function parameter
    column String
    (string) - Source column whose value is passed for this parameter at execution time
    parameter String
    (string) - Name of the UC function parameter
    column string
    (string) - Source column whose value is passed for this parameter at execution time
    parameter string
    (string) - Name of the UC function parameter
    column str
    (string) - Source column whose value is passed for this parameter at execution time
    parameter str
    (string) - Name of the UC function parameter
    column String
    (string) - Source column whose value is passed for this parameter at execution time
    parameter String
    (string) - Name of the UC function parameter

    GetFeatureEngineeringFeatureLineageContext

    JobContext GetFeatureEngineeringFeatureLineageContextJobContext
    (JobContext) - Job context information including job ID and run ID
    NotebookId int
    (integer) - The notebook ID where this API was invoked
    JobContext GetFeatureEngineeringFeatureLineageContextJobContext
    (JobContext) - Job context information including job ID and run ID
    NotebookId int
    (integer) - The notebook ID where this API was invoked
    job_context object
    (JobContext) - Job context information including job ID and run ID
    notebook_id number
    (integer) - The notebook ID where this API was invoked
    jobContext GetFeatureEngineeringFeatureLineageContextJobContext
    (JobContext) - Job context information including job ID and run ID
    notebookId Integer
    (integer) - The notebook ID where this API was invoked
    jobContext GetFeatureEngineeringFeatureLineageContextJobContext
    (JobContext) - Job context information including job ID and run ID
    notebookId number
    (integer) - The notebook ID where this API was invoked
    job_context GetFeatureEngineeringFeatureLineageContextJobContext
    (JobContext) - Job context information including job ID and run ID
    notebook_id int
    (integer) - The notebook ID where this API was invoked
    jobContext Property Map
    (JobContext) - Job context information including job ID and run ID
    notebookId Number
    (integer) - The notebook ID where this API was invoked

    GetFeatureEngineeringFeatureLineageContextJobContext

    JobId int
    (integer) - The job ID where this API invoked
    JobRunId int
    (integer) - The job run ID where this API was invoked
    JobId int
    (integer) - The job ID where this API invoked
    JobRunId int
    (integer) - The job run ID where this API was invoked
    job_id number
    (integer) - The job ID where this API invoked
    job_run_id number
    (integer) - The job run ID where this API was invoked
    jobId Integer
    (integer) - The job ID where this API invoked
    jobRunId Integer
    (integer) - The job run ID where this API was invoked
    jobId number
    (integer) - The job ID where this API invoked
    jobRunId number
    (integer) - The job run ID where this API was invoked
    job_id int
    (integer) - The job ID where this API invoked
    job_run_id int
    (integer) - The job run ID where this API was invoked
    jobId Number
    (integer) - The job ID where this API invoked
    jobRunId Number
    (integer) - The job run ID where this API was invoked

    GetFeatureEngineeringFeatureProviderConfig

    WorkspaceId string
    Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.
    WorkspaceId string
    Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.
    workspace_id string
    Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.
    workspaceId String
    Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.
    workspaceId string
    Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.
    workspace_id str
    Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.
    workspaceId String
    Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.

    GetFeatureEngineeringFeatureSource

    DeltaTableSource GetFeatureEngineeringFeatureSourceDeltaTableSource
    (DeltaTableSource) - A Delta table data source
    KafkaSource GetFeatureEngineeringFeatureSourceKafkaSource
    (KafkaSource) - A Kafka stream data source
    Lateness GetFeatureEngineeringFeatureSourceLateness
    (SourceLateness) - Completeness timing for this Feature's use of the source. This configuration is part of the Feature definition; it does not modify the underlying table or stream
    RequestSource GetFeatureEngineeringFeatureSourceRequestSource
    (RequestSource) - A request-time data source
    StreamSource GetFeatureEngineeringFeatureSourceStreamSource
    (StreamSource) - A Stream data source
    DeltaTableSource GetFeatureEngineeringFeatureSourceDeltaTableSource
    (DeltaTableSource) - A Delta table data source
    KafkaSource GetFeatureEngineeringFeatureSourceKafkaSource
    (KafkaSource) - A Kafka stream data source
    Lateness GetFeatureEngineeringFeatureSourceLateness
    (SourceLateness) - Completeness timing for this Feature's use of the source. This configuration is part of the Feature definition; it does not modify the underlying table or stream
    RequestSource GetFeatureEngineeringFeatureSourceRequestSource
    (RequestSource) - A request-time data source
    StreamSource GetFeatureEngineeringFeatureSourceStreamSource
    (StreamSource) - A Stream data source
    delta_table_source object
    (DeltaTableSource) - A Delta table data source
    kafka_source object
    (KafkaSource) - A Kafka stream data source
    lateness object
    (SourceLateness) - Completeness timing for this Feature's use of the source. This configuration is part of the Feature definition; it does not modify the underlying table or stream
    request_source object
    (RequestSource) - A request-time data source
    stream_source object
    (StreamSource) - A Stream data source
    deltaTableSource GetFeatureEngineeringFeatureSourceDeltaTableSource
    (DeltaTableSource) - A Delta table data source
    kafkaSource GetFeatureEngineeringFeatureSourceKafkaSource
    (KafkaSource) - A Kafka stream data source
    lateness GetFeatureEngineeringFeatureSourceLateness
    (SourceLateness) - Completeness timing for this Feature's use of the source. This configuration is part of the Feature definition; it does not modify the underlying table or stream
    requestSource GetFeatureEngineeringFeatureSourceRequestSource
    (RequestSource) - A request-time data source
    streamSource GetFeatureEngineeringFeatureSourceStreamSource
    (StreamSource) - A Stream data source
    deltaTableSource GetFeatureEngineeringFeatureSourceDeltaTableSource
    (DeltaTableSource) - A Delta table data source
    kafkaSource GetFeatureEngineeringFeatureSourceKafkaSource
    (KafkaSource) - A Kafka stream data source
    lateness GetFeatureEngineeringFeatureSourceLateness
    (SourceLateness) - Completeness timing for this Feature's use of the source. This configuration is part of the Feature definition; it does not modify the underlying table or stream
    requestSource GetFeatureEngineeringFeatureSourceRequestSource
    (RequestSource) - A request-time data source
    streamSource GetFeatureEngineeringFeatureSourceStreamSource
    (StreamSource) - A Stream data source
    delta_table_source GetFeatureEngineeringFeatureSourceDeltaTableSource
    (DeltaTableSource) - A Delta table data source
    kafka_source GetFeatureEngineeringFeatureSourceKafkaSource
    (KafkaSource) - A Kafka stream data source
    lateness GetFeatureEngineeringFeatureSourceLateness
    (SourceLateness) - Completeness timing for this Feature's use of the source. This configuration is part of the Feature definition; it does not modify the underlying table or stream
    request_source GetFeatureEngineeringFeatureSourceRequestSource
    (RequestSource) - A request-time data source
    stream_source GetFeatureEngineeringFeatureSourceStreamSource
    (StreamSource) - A Stream data source
    deltaTableSource Property Map
    (DeltaTableSource) - A Delta table data source
    kafkaSource Property Map
    (KafkaSource) - A Kafka stream data source
    lateness Property Map
    (SourceLateness) - Completeness timing for this Feature's use of the source. This configuration is part of the Feature definition; it does not modify the underlying table or stream
    requestSource Property Map
    (RequestSource) - A request-time data source
    streamSource Property Map
    (StreamSource) - A Stream data source

    GetFeatureEngineeringFeatureSourceDeltaTableSource

    FullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    DataframeSchema string
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    FilterCondition string
    (string) - The filter condition applied to the source data before aggregation
    TransformationSql string
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    FullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    DataframeSchema string
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    FilterCondition string
    (string) - The filter condition applied to the source data before aggregation
    TransformationSql string
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    full_name string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframe_schema string
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filter_condition string
    (string) - The filter condition applied to the source data before aggregation
    transformation_sql string
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    fullName String
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframeSchema String
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filterCondition String
    (string) - The filter condition applied to the source data before aggregation
    transformationSql String
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    fullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframeSchema string
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filterCondition string
    (string) - The filter condition applied to the source data before aggregation
    transformationSql string
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    full_name str
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframe_schema str
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filter_condition str
    (string) - The filter condition applied to the source data before aggregation
    transformation_sql str
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    fullName String
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframeSchema String
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filterCondition String
    (string) - The filter condition applied to the source data before aggregation
    transformationSql String
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object

    GetFeatureEngineeringFeatureSourceKafkaSource

    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    FilterCondition string
    (string) - The filter condition applied to the source data before aggregation
    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    FilterCondition string
    (string) - The filter condition applied to the source data before aggregation
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    filter_condition string
    (string) - The filter condition applied to the source data before aggregation
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    filterCondition String
    (string) - The filter condition applied to the source data before aggregation
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    filterCondition string
    (string) - The filter condition applied to the source data before aggregation
    name str
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    filter_condition str
    (string) - The filter condition applied to the source data before aggregation
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    filterCondition String
    (string) - The filter condition applied to the source data before aggregation

    GetFeatureEngineeringFeatureSourceLateness

    SettlingDelay string
    (string) - Non-negative time to wait after a window ends before treating its source data as complete. Training shifts the eligible evaluation time backwards by this duration so it does not join data that would still have been settling online. Materialization waits for the duration to elapse before publishing the window. If unset, source data is considered settled immediately
    SettlingDelay string
    (string) - Non-negative time to wait after a window ends before treating its source data as complete. Training shifts the eligible evaluation time backwards by this duration so it does not join data that would still have been settling online. Materialization waits for the duration to elapse before publishing the window. If unset, source data is considered settled immediately
    settling_delay string
    (string) - Non-negative time to wait after a window ends before treating its source data as complete. Training shifts the eligible evaluation time backwards by this duration so it does not join data that would still have been settling online. Materialization waits for the duration to elapse before publishing the window. If unset, source data is considered settled immediately
    settlingDelay String
    (string) - Non-negative time to wait after a window ends before treating its source data as complete. Training shifts the eligible evaluation time backwards by this duration so it does not join data that would still have been settling online. Materialization waits for the duration to elapse before publishing the window. If unset, source data is considered settled immediately
    settlingDelay string
    (string) - Non-negative time to wait after a window ends before treating its source data as complete. Training shifts the eligible evaluation time backwards by this duration so it does not join data that would still have been settling online. Materialization waits for the duration to elapse before publishing the window. If unset, source data is considered settled immediately
    settling_delay str
    (string) - Non-negative time to wait after a window ends before treating its source data as complete. Training shifts the eligible evaluation time backwards by this duration so it does not join data that would still have been settling online. Materialization waits for the duration to elapse before publishing the window. If unset, source data is considered settled immediately
    settlingDelay String
    (string) - Non-negative time to wait after a window ends before treating its source data as complete. Training shifts the eligible evaluation time backwards by this duration so it does not join data that would still have been settling online. Materialization waits for the duration to elapse before publishing the window. If unset, source data is considered settled immediately

    GetFeatureEngineeringFeatureSourceRequestSource

    FlatSchema GetFeatureEngineeringFeatureSourceRequestSourceFlatSchema
    (FlatSchema) - A flat schema with scalar-typed fields only
    FlatSchema GetFeatureEngineeringFeatureSourceRequestSourceFlatSchema
    (FlatSchema) - A flat schema with scalar-typed fields only
    flat_schema object
    (FlatSchema) - A flat schema with scalar-typed fields only
    flatSchema GetFeatureEngineeringFeatureSourceRequestSourceFlatSchema
    (FlatSchema) - A flat schema with scalar-typed fields only
    flatSchema GetFeatureEngineeringFeatureSourceRequestSourceFlatSchema
    (FlatSchema) - A flat schema with scalar-typed fields only
    flat_schema GetFeatureEngineeringFeatureSourceRequestSourceFlatSchema
    (FlatSchema) - A flat schema with scalar-typed fields only
    flatSchema Property Map
    (FlatSchema) - A flat schema with scalar-typed fields only

    GetFeatureEngineeringFeatureSourceRequestSourceFlatSchema

    Fields List<GetFeatureEngineeringFeatureSourceRequestSourceFlatSchemaField>
    (list of FieldDefinition) - The list of fields in this schema
    Fields []GetFeatureEngineeringFeatureSourceRequestSourceFlatSchemaField
    (list of FieldDefinition) - The list of fields in this schema
    fields list(object)
    (list of FieldDefinition) - The list of fields in this schema
    fields List<GetFeatureEngineeringFeatureSourceRequestSourceFlatSchemaField>
    (list of FieldDefinition) - The list of fields in this schema
    fields GetFeatureEngineeringFeatureSourceRequestSourceFlatSchemaField[]
    (list of FieldDefinition) - The list of fields in this schema
    fields Sequence[GetFeatureEngineeringFeatureSourceRequestSourceFlatSchemaField]
    (list of FieldDefinition) - The list of fields in this schema
    fields List<Property Map>
    (list of FieldDefinition) - The list of fields in this schema

    GetFeatureEngineeringFeatureSourceRequestSourceFlatSchemaField

    DataType string
    (string) - The scalar data type of the field. Possible values are: BINARY, BOOLEAN, DATE, DECIMAL, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIMESTAMP
    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    DataType string
    (string) - The scalar data type of the field. Possible values are: BINARY, BOOLEAN, DATE, DECIMAL, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIMESTAMP
    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    data_type string
    (string) - The scalar data type of the field. Possible values are: BINARY, BOOLEAN, DATE, DECIMAL, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIMESTAMP
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    dataType String
    (string) - The scalar data type of the field. Possible values are: BINARY, BOOLEAN, DATE, DECIMAL, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIMESTAMP
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    dataType string
    (string) - The scalar data type of the field. Possible values are: BINARY, BOOLEAN, DATE, DECIMAL, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIMESTAMP
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    data_type str
    (string) - The scalar data type of the field. Possible values are: BINARY, BOOLEAN, DATE, DECIMAL, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIMESTAMP
    name str
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    dataType String
    (string) - The scalar data type of the field. Possible values are: BINARY, BOOLEAN, DATE, DECIMAL, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIMESTAMP
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation

    GetFeatureEngineeringFeatureSourceStreamSource

    FullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    DataframeSchema string
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    FilterCondition string
    (string) - The filter condition applied to the source data before aggregation
    TransformationSql string
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    FullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    DataframeSchema string
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    FilterCondition string
    (string) - The filter condition applied to the source data before aggregation
    TransformationSql string
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    full_name string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframe_schema string
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filter_condition string
    (string) - The filter condition applied to the source data before aggregation
    transformation_sql string
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    fullName String
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframeSchema String
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filterCondition String
    (string) - The filter condition applied to the source data before aggregation
    transformationSql String
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    fullName string
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframeSchema string
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filterCondition string
    (string) - The filter condition applied to the source data before aggregation
    transformationSql string
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    full_name str
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframe_schema str
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filter_condition str
    (string) - The filter condition applied to the source data before aggregation
    transformation_sql str
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object
    fullName String
    The full three-part name (catalog, schema, name) of the feature. This is the feature's resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value
    dataframeSchema String
    (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Any subsequent functions operate against this dataframe
    filterCondition String
    (string) - The filter condition applied to the source data before aggregation
    transformationSql String
    (string) - The pipeline runs these SQL statements immediately after conversion into the schema specified on the Stream object

    GetFeatureEngineeringFeatureTimeseriesColumn

    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    Name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name string
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name str
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation
    name String
    (string) - The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., "value.event_timestamp"). For nested fields, the leaf node name (e.g., "eventTimestamp" from "value.event_details.event_timestamp") is what will be present in materialized tables and expected to match at query time. Colon-prefixed notation (e.g., "value:event_timestamp") is supported for backwards compatibility but is deprecated; migrate to dot notation

    Package Details

    Repository
    databricks pulumi/pulumi-databricks
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the databricks Terraform Provider.
    databricks logo databricks logo
    Viewing docs for Databricks v1.109.0
    published on Tuesday, Sep 8, 2026 by Pulumi
    Schema (JSON)
    pulumi/pulumi-databricks

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