databricks.getFeatureEngineeringFeatures

Databricks v1.109.0, Sep 8 26

Viewing docs for Databricks v1.109.0
published on Tuesday, Sep 8, 2026 by Pulumi

Schema (JSON)

pulumi/pulumi-databricks

Viewing docs for Databricks v1.109.0
published on Tuesday, Sep 8, 2026 by Pulumi

Schema (JSON)

pulumi/pulumi-databricks

Using getFeatureEngineeringFeatures

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 getFeatureEngineeringFeatures(args: GetFeatureEngineeringFeaturesArgs, opts?: InvokeOptions): Promise<GetFeatureEngineeringFeaturesResult>
function getFeatureEngineeringFeaturesOutput(args: GetFeatureEngineeringFeaturesOutputArgs, opts?: InvokeOutputOptions): Output<GetFeatureEngineeringFeaturesResult>

def get_feature_engineering_features(catalog_name: Optional[str] = None,
                                     page_size: Optional[int] = None,
                                     provider_config: Optional[GetFeatureEngineeringFeaturesProviderConfig] = None,
                                     schema_name: Optional[str] = None,
                                     opts: Optional[InvokeOptions] = None) -> GetFeatureEngineeringFeaturesResult
def get_feature_engineering_features_output(catalog_name: pulumi.Input[Optional[str]] = None,
                                     page_size: pulumi.Input[Optional[int]] = None,
                                     provider_config: pulumi.Input[Optional[GetFeatureEngineeringFeaturesProviderConfigArgs]] = None,
                                     schema_name: pulumi.Input[Optional[str]] = None,
                                     opts: Optional[InvokeOutputOptions] = None) -> Output[GetFeatureEngineeringFeaturesResult]

func GetFeatureEngineeringFeatures(ctx *Context, args *GetFeatureEngineeringFeaturesArgs, opts ...InvokeOption) (*GetFeatureEngineeringFeaturesResult, error)
func GetFeatureEngineeringFeaturesOutput(ctx *Context, args *GetFeatureEngineeringFeaturesOutputArgs, opts ...InvokeOption) GetFeatureEngineeringFeaturesResultOutput

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

public static class GetFeatureEngineeringFeatures 
{
    public static Task<GetFeatureEngineeringFeaturesResult> InvokeAsync(GetFeatureEngineeringFeaturesArgs args, InvokeOptions? opts = null)
    public static Output<GetFeatureEngineeringFeaturesResult> Invoke(GetFeatureEngineeringFeaturesInvokeArgs args, InvokeOptions? opts = null)
    public static Output<GetFeatureEngineeringFeaturesResult> Invoke(GetFeatureEngineeringFeaturesInvokeArgs args, InvokeOutputOptions opts)
}

public static CompletableFuture<GetFeatureEngineeringFeaturesResult> getFeatureEngineeringFeatures(GetFeatureEngineeringFeaturesArgs args, InvokeOptions options)
public static Output<GetFeatureEngineeringFeaturesResult> getFeatureEngineeringFeatures(GetFeatureEngineeringFeaturesArgs args, InvokeOptions options)
public static Output<GetFeatureEngineeringFeaturesResult> getFeatureEngineeringFeatures(GetFeatureEngineeringFeaturesArgs args, InvokeOutputOptions options)

fn::invoke:
  function: databricks:index/getFeatureEngineeringFeatures:getFeatureEngineeringFeatures
  arguments:
    # arguments dictionary

data "databricks_get_feature_engineering_features" "name" {
    # arguments
}

The following arguments are supported:

CatalogName string: Name of parent catalog for features of interest
SchemaName string: Name of parent schema relative to its parent catalog
PageSize int: The maximum number of results to return
ProviderConfig GetFeatureEngineeringFeaturesProviderConfig: Configure the provider for management through account provider.

CatalogName string: Name of parent catalog for features of interest
SchemaName string: Name of parent schema relative to its parent catalog
PageSize int: The maximum number of results to return
ProviderConfig GetFeatureEngineeringFeaturesProviderConfig: Configure the provider for management through account provider.

catalog_name string: Name of parent catalog for features of interest
schema_name string: Name of parent schema relative to its parent catalog
page_size number: The maximum number of results to return
provider_config object: Configure the provider for management through account provider.

catalogName String: Name of parent catalog for features of interest
schemaName String: Name of parent schema relative to its parent catalog
pageSize Integer: The maximum number of results to return
providerConfig GetFeatureEngineeringFeaturesProviderConfig: Configure the provider for management through account provider.

catalogName string: Name of parent catalog for features of interest
schemaName string: Name of parent schema relative to its parent catalog
pageSize number: The maximum number of results to return
providerConfig GetFeatureEngineeringFeaturesProviderConfig: Configure the provider for management through account provider.

catalog_name str: Name of parent catalog for features of interest
schema_name str: Name of parent schema relative to its parent catalog
page_size int: The maximum number of results to return
provider_config GetFeatureEngineeringFeaturesProviderConfig: Configure the provider for management through account provider.

catalogName String: Name of parent catalog for features of interest
schemaName String: Name of parent schema relative to its parent catalog
pageSize Number: The maximum number of results to return
providerConfig Property Map: Configure the provider for management through account provider.

getFeatureEngineeringFeatures Result

The following output properties are available:

CatalogName string: (string) - Name of parent catalog
Features List<GetFeatureEngineeringFeaturesFeature>
SchemaName string: (string) - Name of parent schema relative to its parent catalog
PageSize int
ProviderConfig GetFeatureEngineeringFeaturesProviderConfig

CatalogName string: (string) - Name of parent catalog
Features []GetFeatureEngineeringFeaturesFeature
SchemaName string: (string) - Name of parent schema relative to its parent catalog
PageSize int
ProviderConfig GetFeatureEngineeringFeaturesProviderConfig

catalog_name string: (string) - Name of parent catalog
features list(object)
schema_name string: (string) - Name of parent schema relative to its parent catalog
page_size number
provider_config object

catalogName String: (string) - Name of parent catalog
features List<GetFeatureEngineeringFeaturesFeature>
schemaName String: (string) - Name of parent schema relative to its parent catalog
pageSize Integer
providerConfig GetFeatureEngineeringFeaturesProviderConfig

catalogName string: (string) - Name of parent catalog
features GetFeatureEngineeringFeaturesFeature[]
schemaName string: (string) - Name of parent schema relative to its parent catalog
pageSize number
providerConfig GetFeatureEngineeringFeaturesProviderConfig

catalog_name str: (string) - Name of parent catalog
features Sequence[GetFeatureEngineeringFeaturesFeature]
schema_name str: (string) - Name of parent schema relative to its parent catalog
page_size int
provider_config GetFeatureEngineeringFeaturesProviderConfig

catalogName String: (string) - Name of parent catalog
features List<Property Map>
schemaName String: (string) - Name of parent schema relative to its parent catalog
pageSize Number
providerConfig Property Map

Supporting Types

GetFeatureEngineeringFeaturesFeature

CatalogName string: Name of parent catalog for features of interest
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<GetFeatureEngineeringFeaturesFeatureEntity>: (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 GetFeatureEngineeringFeaturesFeatureFunction: (Function) - The function by which the feature is computed
LineageContext GetFeatureEngineeringFeaturesFeatureLineageContext: (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: Name of parent schema relative to its parent catalog
Source GetFeatureEngineeringFeaturesFeatureSource: (DataSource) - The data source of the feature
TimeseriesColumn GetFeatureEngineeringFeaturesFeatureTimeseriesColumn: (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
ProviderConfig GetFeatureEngineeringFeaturesFeatureProviderConfig: Configure the provider for management through account provider.

CatalogName string: Name of parent catalog for features of interest
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 []GetFeatureEngineeringFeaturesFeatureEntity: (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 GetFeatureEngineeringFeaturesFeatureFunction: (Function) - The function by which the feature is computed
LineageContext GetFeatureEngineeringFeaturesFeatureLineageContext: (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: Name of parent schema relative to its parent catalog
Source GetFeatureEngineeringFeaturesFeatureSource: (DataSource) - The data source of the feature
TimeseriesColumn GetFeatureEngineeringFeaturesFeatureTimeseriesColumn: (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
ProviderConfig GetFeatureEngineeringFeaturesFeatureProviderConfig: Configure the provider for management through account provider.

catalog_name string: Name of parent catalog for features of interest
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: 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: Configure the provider for management through account provider.

catalogName String: Name of parent catalog for features of interest
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<GetFeatureEngineeringFeaturesFeatureEntity>: (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 GetFeatureEngineeringFeaturesFeatureFunction: (Function) - The function by which the feature is computed
lineageContext GetFeatureEngineeringFeaturesFeatureLineageContext: (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: Name of parent schema relative to its parent catalog
source GetFeatureEngineeringFeaturesFeatureSource: (DataSource) - The data source of the feature
timeseriesColumn GetFeatureEngineeringFeaturesFeatureTimeseriesColumn: (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
providerConfig GetFeatureEngineeringFeaturesFeatureProviderConfig: Configure the provider for management through account provider.

catalogName string: Name of parent catalog for features of interest
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 GetFeatureEngineeringFeaturesFeatureEntity[]: (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 GetFeatureEngineeringFeaturesFeatureFunction: (Function) - The function by which the feature is computed
lineageContext GetFeatureEngineeringFeaturesFeatureLineageContext: (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: Name of parent schema relative to its parent catalog
source GetFeatureEngineeringFeaturesFeatureSource: (DataSource) - The data source of the feature
timeseriesColumn GetFeatureEngineeringFeaturesFeatureTimeseriesColumn: (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
providerConfig GetFeatureEngineeringFeaturesFeatureProviderConfig: Configure the provider for management through account provider.

catalog_name str: Name of parent catalog for features of interest
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[GetFeatureEngineeringFeaturesFeatureEntity]: (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 GetFeatureEngineeringFeaturesFeatureFunction: (Function) - The function by which the feature is computed
lineage_context GetFeatureEngineeringFeaturesFeatureLineageContext: (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: Name of parent schema relative to its parent catalog
source GetFeatureEngineeringFeaturesFeatureSource: (DataSource) - The data source of the feature
timeseries_column GetFeatureEngineeringFeaturesFeatureTimeseriesColumn: (TimeseriesColumn) - Column recording time, used for point-in-time joins, backfills, and aggregations
provider_config GetFeatureEngineeringFeaturesFeatureProviderConfig: Configure the provider for management through account provider.

catalogName String: Name of parent catalog for features of interest
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: 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: Configure the provider for management through account provider.

GetFeatureEngineeringFeaturesFeatureEntity

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

GetFeatureEngineeringFeaturesFeatureFunction

AggregationFunction GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunction: (AggregationFunction) - An aggregation function applied over a time window
ColumnSelection GetFeatureEngineeringFeaturesFeatureFunctionColumnSelection: (ColumnSelection) - Selects the latest value of a single column in a data source
CustomUdf GetFeatureEngineeringFeaturesFeatureFunctionCustomUdf: (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns

AggregationFunction GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunction: (AggregationFunction) - An aggregation function applied over a time window
ColumnSelection GetFeatureEngineeringFeaturesFeatureFunctionColumnSelection: (ColumnSelection) - Selects the latest value of a single column in a data source
CustomUdf GetFeatureEngineeringFeaturesFeatureFunctionCustomUdf: (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 GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunction: (AggregationFunction) - An aggregation function applied over a time window
columnSelection GetFeatureEngineeringFeaturesFeatureFunctionColumnSelection: (ColumnSelection) - Selects the latest value of a single column in a data source
customUdf GetFeatureEngineeringFeaturesFeatureFunctionCustomUdf: (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns

aggregationFunction GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunction: (AggregationFunction) - An aggregation function applied over a time window
columnSelection GetFeatureEngineeringFeaturesFeatureFunctionColumnSelection: (ColumnSelection) - Selects the latest value of a single column in a data source
customUdf GetFeatureEngineeringFeaturesFeatureFunctionCustomUdf: (CustomUdf) - Applies a registered Unity Catalog function row-wise to source columns

aggregation_function GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunction: (AggregationFunction) - An aggregation function applied over a time window
column_selection GetFeatureEngineeringFeaturesFeatureFunctionColumnSelection: (ColumnSelection) - Selects the latest value of a single column in a data source
custom_udf GetFeatureEngineeringFeaturesFeatureFunctionCustomUdf: (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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunction

ApproxCountDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxCountDistinct: (ApproxCountDistinctFunction)
ApproxPercentile GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxPercentile: (ApproxPercentileFunction)
Avg GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionAvg: (AvgFunction)
CountFunction GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionCountFunction: (CountFunction)
First GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirst: (FirstFunction)
FirstDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstDistinct: (FirstDistinctFunction)
FirstN GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstN: (FirstNFunction)
Last GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLast: (LastFunction)
LastDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastDistinct: (LastDistinctFunction)
LastN GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastN: (LastNFunction)
Max GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMax: (MaxFunction)
Min GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMin: (MinFunction)
StddevPop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevPop: (StddevPopFunction)
StddevSamp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevSamp: (StddevSampFunction)
Sum GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionSum: (SumFunction)
TimeWindow GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindow: (TimeWindow) - The time window over which the aggregation is computed
VarPop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarPop: (VarPopFunction)
VarSamp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarSamp: (VarSampFunction)

ApproxCountDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxCountDistinct: (ApproxCountDistinctFunction)
ApproxPercentile GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxPercentile: (ApproxPercentileFunction)
Avg GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionAvg: (AvgFunction)
CountFunction GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionCountFunction: (CountFunction)
First GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirst: (FirstFunction)
FirstDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstDistinct: (FirstDistinctFunction)
FirstN GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstN: (FirstNFunction)
Last GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLast: (LastFunction)
LastDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastDistinct: (LastDistinctFunction)
LastN GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastN: (LastNFunction)
Max GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMax: (MaxFunction)
Min GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMin: (MinFunction)
StddevPop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevPop: (StddevPopFunction)
StddevSamp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevSamp: (StddevSampFunction)
Sum GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionSum: (SumFunction)
TimeWindow GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindow: (TimeWindow) - The time window over which the aggregation is computed
VarPop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarPop: (VarPopFunction)
VarSamp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarSamp: (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 GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxCountDistinct: (ApproxCountDistinctFunction)
approxPercentile GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxPercentile: (ApproxPercentileFunction)
avg GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionAvg: (AvgFunction)
countFunction GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionCountFunction: (CountFunction)
first GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirst: (FirstFunction)
firstDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstDistinct: (FirstDistinctFunction)
firstN GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstN: (FirstNFunction)
last GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLast: (LastFunction)
lastDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastDistinct: (LastDistinctFunction)
lastN GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastN: (LastNFunction)
max GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMax: (MaxFunction)
min GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMin: (MinFunction)
stddevPop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevPop: (StddevPopFunction)
stddevSamp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevSamp: (StddevSampFunction)
sum GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionSum: (SumFunction)
timeWindow GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindow: (TimeWindow) - The time window over which the aggregation is computed
varPop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarPop: (VarPopFunction)
varSamp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarSamp: (VarSampFunction)

approxCountDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxCountDistinct: (ApproxCountDistinctFunction)
approxPercentile GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxPercentile: (ApproxPercentileFunction)
avg GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionAvg: (AvgFunction)
countFunction GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionCountFunction: (CountFunction)
first GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirst: (FirstFunction)
firstDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstDistinct: (FirstDistinctFunction)
firstN GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstN: (FirstNFunction)
last GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLast: (LastFunction)
lastDistinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastDistinct: (LastDistinctFunction)
lastN GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastN: (LastNFunction)
max GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMax: (MaxFunction)
min GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMin: (MinFunction)
stddevPop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevPop: (StddevPopFunction)
stddevSamp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevSamp: (StddevSampFunction)
sum GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionSum: (SumFunction)
timeWindow GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindow: (TimeWindow) - The time window over which the aggregation is computed
varPop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarPop: (VarPopFunction)
varSamp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarSamp: (VarSampFunction)

approx_count_distinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxCountDistinct: (ApproxCountDistinctFunction)
approx_percentile GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxPercentile: (ApproxPercentileFunction)
avg GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionAvg: (AvgFunction)
count_function GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionCountFunction: (CountFunction)
first GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirst: (FirstFunction)
first_distinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstDistinct: (FirstDistinctFunction)
first_n GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstN: (FirstNFunction)
last GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLast: (LastFunction)
last_distinct GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastDistinct: (LastDistinctFunction)
last_n GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastN: (LastNFunction)
max GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMax: (MaxFunction)
min GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMin: (MinFunction)
stddev_pop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevPop: (StddevPopFunction)
stddev_samp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevSamp: (StddevSampFunction)
sum GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionSum: (SumFunction)
time_window GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindow: (TimeWindow) - The time window over which the aggregation is computed
var_pop GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarPop: (VarPopFunction)
var_samp GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarSamp: (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)

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxCountDistinct

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)

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxPercentile

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)

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionAvg

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionCountFunction

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirst

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstDistinct

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstN

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLast

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastDistinct

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastN

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMax

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMin

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevPop

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevSamp

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionSum

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindow

Rolling GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowRolling: (RollingWindow)
Sawtooth GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSawtooth: (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
Sliding GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSliding: (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 GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowTumbling: (TumblingWindow)

Rolling GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowRolling: (RollingWindow)
Sawtooth GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSawtooth: (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
Sliding GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSliding: (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 GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowTumbling: (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 GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowRolling: (RollingWindow)
sawtooth GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSawtooth: (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
sliding GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSliding: (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 GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowTumbling: (TumblingWindow)

rolling GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowRolling: (RollingWindow)
sawtooth GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSawtooth: (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
sliding GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSliding: (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 GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowTumbling: (TumblingWindow)

rolling GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowRolling: (RollingWindow)
sawtooth GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSawtooth: (SawtoothWindow) - A sawtooth window served via the hybrid batch + streaming path
sliding GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSliding: (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 GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowTumbling: (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)

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowRolling

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)

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSawtooth

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)

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSliding

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)

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowTumbling

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarPop

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

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarSamp

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

GetFeatureEngineeringFeaturesFeatureFunctionColumnSelection

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

GetFeatureEngineeringFeaturesFeatureFunctionCustomUdf

FunctionPath string: (string) - Fully qualified 3-part Unity Catalog path of the function to apply
InputBindings List<GetFeatureEngineeringFeaturesFeatureFunctionCustomUdfInputBinding>: (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 []GetFeatureEngineeringFeaturesFeatureFunctionCustomUdfInputBinding: (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<GetFeatureEngineeringFeaturesFeatureFunctionCustomUdfInputBinding>: (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 GetFeatureEngineeringFeaturesFeatureFunctionCustomUdfInputBinding[]: (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[GetFeatureEngineeringFeaturesFeatureFunctionCustomUdfInputBinding]: (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)

GetFeatureEngineeringFeaturesFeatureFunctionCustomUdfInputBinding

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

GetFeatureEngineeringFeaturesFeatureLineageContext

JobContext GetFeatureEngineeringFeaturesFeatureLineageContextJobContext: (JobContext) - Job context information including job ID and run ID
NotebookId int: (integer) - The notebook ID where this API was invoked

JobContext GetFeatureEngineeringFeaturesFeatureLineageContextJobContext: (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 GetFeatureEngineeringFeaturesFeatureLineageContextJobContext: (JobContext) - Job context information including job ID and run ID
notebookId Integer: (integer) - The notebook ID where this API was invoked

jobContext GetFeatureEngineeringFeaturesFeatureLineageContextJobContext: (JobContext) - Job context information including job ID and run ID
notebookId number: (integer) - The notebook ID where this API was invoked

job_context GetFeatureEngineeringFeaturesFeatureLineageContextJobContext: (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

GetFeatureEngineeringFeaturesFeatureLineageContextJobContext

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

GetFeatureEngineeringFeaturesFeatureProviderConfig

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.

GetFeatureEngineeringFeaturesFeatureSource

DeltaTableSource GetFeatureEngineeringFeaturesFeatureSourceDeltaTableSource: (DeltaTableSource) - A Delta table data source
KafkaSource GetFeatureEngineeringFeaturesFeatureSourceKafkaSource: (KafkaSource) - A Kafka stream data source
Lateness GetFeatureEngineeringFeaturesFeatureSourceLateness: (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 GetFeatureEngineeringFeaturesFeatureSourceRequestSource: (RequestSource) - A request-time data source
StreamSource GetFeatureEngineeringFeaturesFeatureSourceStreamSource: (StreamSource) - A Stream data source

DeltaTableSource GetFeatureEngineeringFeaturesFeatureSourceDeltaTableSource: (DeltaTableSource) - A Delta table data source
KafkaSource GetFeatureEngineeringFeaturesFeatureSourceKafkaSource: (KafkaSource) - A Kafka stream data source
Lateness GetFeatureEngineeringFeaturesFeatureSourceLateness: (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 GetFeatureEngineeringFeaturesFeatureSourceRequestSource: (RequestSource) - A request-time data source
StreamSource GetFeatureEngineeringFeaturesFeatureSourceStreamSource: (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 GetFeatureEngineeringFeaturesFeatureSourceDeltaTableSource: (DeltaTableSource) - A Delta table data source
kafkaSource GetFeatureEngineeringFeaturesFeatureSourceKafkaSource: (KafkaSource) - A Kafka stream data source
lateness GetFeatureEngineeringFeaturesFeatureSourceLateness: (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 GetFeatureEngineeringFeaturesFeatureSourceRequestSource: (RequestSource) - A request-time data source
streamSource GetFeatureEngineeringFeaturesFeatureSourceStreamSource: (StreamSource) - A Stream data source

deltaTableSource GetFeatureEngineeringFeaturesFeatureSourceDeltaTableSource: (DeltaTableSource) - A Delta table data source
kafkaSource GetFeatureEngineeringFeaturesFeatureSourceKafkaSource: (KafkaSource) - A Kafka stream data source
lateness GetFeatureEngineeringFeaturesFeatureSourceLateness: (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 GetFeatureEngineeringFeaturesFeatureSourceRequestSource: (RequestSource) - A request-time data source
streamSource GetFeatureEngineeringFeaturesFeatureSourceStreamSource: (StreamSource) - A Stream data source

delta_table_source GetFeatureEngineeringFeaturesFeatureSourceDeltaTableSource: (DeltaTableSource) - A Delta table data source
kafka_source GetFeatureEngineeringFeaturesFeatureSourceKafkaSource: (KafkaSource) - A Kafka stream data source
lateness GetFeatureEngineeringFeaturesFeatureSourceLateness: (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 GetFeatureEngineeringFeaturesFeatureSourceRequestSource: (RequestSource) - A request-time data source
stream_source GetFeatureEngineeringFeaturesFeatureSourceStreamSource: (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

GetFeatureEngineeringFeaturesFeatureSourceDeltaTableSource

FullName string: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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

GetFeatureEngineeringFeaturesFeatureSourceKafkaSource

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

GetFeatureEngineeringFeaturesFeatureSourceLateness

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

GetFeatureEngineeringFeaturesFeatureSourceRequestSource

FlatSchema GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchema: (FlatSchema) - A flat schema with scalar-typed fields only

FlatSchema GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchema: (FlatSchema) - A flat schema with scalar-typed fields only

flat_schema object: (FlatSchema) - A flat schema with scalar-typed fields only

flatSchema GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchema: (FlatSchema) - A flat schema with scalar-typed fields only

flatSchema GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchema: (FlatSchema) - A flat schema with scalar-typed fields only

flat_schema GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchema: (FlatSchema) - A flat schema with scalar-typed fields only

flatSchema Property Map: (FlatSchema) - A flat schema with scalar-typed fields only

GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchema

Fields List<GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchemaField>: (list of FieldDefinition) - The list of fields in this schema

Fields []GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchemaField: (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<GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchemaField>: (list of FieldDefinition) - The list of fields in this schema

fields GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchemaField[]: (list of FieldDefinition) - The list of fields in this schema

fields Sequence[GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchemaField]: (list of FieldDefinition) - The list of fields in this schema

fields List<Property Map>: (list of FieldDefinition) - The list of fields in this schema

GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchemaField

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

GetFeatureEngineeringFeaturesFeatureSourceStreamSource

FullName string: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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: (string) - Three-part full name of the Stream (catalog.schema.stream)
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

GetFeatureEngineeringFeaturesFeatureTimeseriesColumn

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

GetFeatureEngineeringFeaturesProviderConfig

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.

Package Details

Repository: databricks pulumi/pulumi-databricks
License: Apache-2.0
Notes: This Pulumi package is based on the databricks Terraform Provider.

Viewing docs for Databricks v1.109.0
published on Tuesday, Sep 8, 2026 by Pulumi

Schema (JSON)

pulumi/pulumi-databricks

databricks.getFeatureEngineeringFeatures

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Using getFeatureEngineeringFeatures

getFeatureEngineeringFeatures Result

Supporting Types

GetFeatureEngineeringFeaturesFeature

GetFeatureEngineeringFeaturesFeatureEntity

GetFeatureEngineeringFeaturesFeatureFunction

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunction

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxCountDistinct

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionApproxPercentile

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionAvg

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionCountFunction

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirst

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstDistinct

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionFirstN

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLast

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastDistinct

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionLastN

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMax

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionMin

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevPop

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionStddevSamp

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionSum

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindow

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowRolling

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSawtooth

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowSliding

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionTimeWindowTumbling

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarPop

GetFeatureEngineeringFeaturesFeatureFunctionAggregationFunctionVarSamp

GetFeatureEngineeringFeaturesFeatureFunctionColumnSelection

GetFeatureEngineeringFeaturesFeatureFunctionCustomUdf

GetFeatureEngineeringFeaturesFeatureFunctionCustomUdfInputBinding

GetFeatureEngineeringFeaturesFeatureLineageContext

GetFeatureEngineeringFeaturesFeatureLineageContextJobContext

GetFeatureEngineeringFeaturesFeatureProviderConfig

GetFeatureEngineeringFeaturesFeatureSource

GetFeatureEngineeringFeaturesFeatureSourceDeltaTableSource

GetFeatureEngineeringFeaturesFeatureSourceKafkaSource

GetFeatureEngineeringFeaturesFeatureSourceLateness

GetFeatureEngineeringFeaturesFeatureSourceRequestSource

GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchema

GetFeatureEngineeringFeaturesFeatureSourceRequestSourceFlatSchemaField

GetFeatureEngineeringFeaturesFeatureSourceStreamSource

GetFeatureEngineeringFeaturesFeatureTimeseriesColumn

GetFeatureEngineeringFeaturesProviderConfig

Package Details

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