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Databricks v1.83.0 published on Friday, Jan 23, 2026 by Pulumi
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pulumi/pulumi-databricks

databricks.FeatureEngineeringFeature

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Databricks v1.83.0 published on Friday, Jan 23, 2026 by Pulumi
Schema (JSON)
pulumi/pulumi-databricks

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    Create FeatureEngineeringFeature Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new FeatureEngineeringFeature(name: string, args: FeatureEngineeringFeatureArgs, opts?: CustomResourceOptions);
    @overload
def FeatureEngineeringFeature(resource_name: str,
                              args: FeatureEngineeringFeatureArgs,
                              opts: Optional[ResourceOptions] = None)

@overload
def FeatureEngineeringFeature(resource_name: str,
                              opts: Optional[ResourceOptions] = None,
                              full_name: Optional[str] = None,
                              function: Optional[FeatureEngineeringFeatureFunctionArgs] = None,
                              inputs: Optional[Sequence[str]] = None,
                              source: Optional[FeatureEngineeringFeatureSourceArgs] = None,
                              description: Optional[str] = None,
                              filter_condition: Optional[str] = None,
                              lineage_context: Optional[FeatureEngineeringFeatureLineageContextArgs] = None,
                              time_window: Optional[FeatureEngineeringFeatureTimeWindowArgs] = None)
    func NewFeatureEngineeringFeature(ctx *Context, name string, args FeatureEngineeringFeatureArgs, opts ...ResourceOption) (*FeatureEngineeringFeature, error)
    public FeatureEngineeringFeature(string name, FeatureEngineeringFeatureArgs args, CustomResourceOptions? opts = null)
    public FeatureEngineeringFeature(String name, FeatureEngineeringFeatureArgs args)
public FeatureEngineeringFeature(String name, FeatureEngineeringFeatureArgs args, CustomResourceOptions options)

    type: databricks:FeatureEngineeringFeature
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args FeatureEngineeringFeatureArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args FeatureEngineeringFeatureArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args FeatureEngineeringFeatureArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args FeatureEngineeringFeatureArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args FeatureEngineeringFeatureArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var featureEngineeringFeatureResource = new Databricks.FeatureEngineeringFeature("featureEngineeringFeatureResource", new()
{
    FullName = "string",
    Function = new Databricks.Inputs.FeatureEngineeringFeatureFunctionArgs
    {
        FunctionType = "string",
        ExtraParameters = new[]
        {
            new Databricks.Inputs.FeatureEngineeringFeatureFunctionExtraParameterArgs
            {
                Key = "string",
                Value = "string",
            },
        },
    },
    Inputs = new[]
    {
        "string",
    },
    Source = new Databricks.Inputs.FeatureEngineeringFeatureSourceArgs
    {
        DeltaTableSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceDeltaTableSourceArgs
        {
            EntityColumns = new[]
            {
                "string",
            },
            FullName = "string",
            TimeseriesColumn = "string",
        },
        KafkaSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceKafkaSourceArgs
        {
            EntityColumnIdentifiers = new[]
            {
                new Databricks.Inputs.FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArgs
                {
                    VariantExprPath = "string",
                },
            },
            Name = "string",
            TimeseriesColumnIdentifier = new Databricks.Inputs.FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifierArgs
            {
                VariantExprPath = "string",
            },
        },
    },
    Description = "string",
    FilterCondition = "string",
    LineageContext = new Databricks.Inputs.FeatureEngineeringFeatureLineageContextArgs
    {
        JobContext = new Databricks.Inputs.FeatureEngineeringFeatureLineageContextJobContextArgs
        {
            JobId = 0,
            JobRunId = 0,
        },
        NotebookId = 0,
    },
    TimeWindow = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowArgs
    {
        Continuous = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowContinuousArgs
        {
            WindowDuration = "string",
            Offset = "string",
        },
        Sliding = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowSlidingArgs
        {
            SlideDuration = "string",
            WindowDuration = "string",
        },
        Tumbling = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowTumblingArgs
        {
            WindowDuration = "string",
        },
    },
});

    example, err := databricks.NewFeatureEngineeringFeature(ctx, "featureEngineeringFeatureResource", &databricks.FeatureEngineeringFeatureArgs{
	FullName: pulumi.String("string"),
	Function: &databricks.FeatureEngineeringFeatureFunctionArgs{
		FunctionType: pulumi.String("string"),
		ExtraParameters: databricks.FeatureEngineeringFeatureFunctionExtraParameterArray{
			&databricks.FeatureEngineeringFeatureFunctionExtraParameterArgs{
				Key:   pulumi.String("string"),
				Value: pulumi.String("string"),
			},
		},
	},
	Inputs: pulumi.StringArray{
		pulumi.String("string"),
	},
	Source: &databricks.FeatureEngineeringFeatureSourceArgs{
		DeltaTableSource: &databricks.FeatureEngineeringFeatureSourceDeltaTableSourceArgs{
			EntityColumns: pulumi.StringArray{
				pulumi.String("string"),
			},
			FullName:         pulumi.String("string"),
			TimeseriesColumn: pulumi.String("string"),
		},
		KafkaSource: &databricks.FeatureEngineeringFeatureSourceKafkaSourceArgs{
			EntityColumnIdentifiers: databricks.FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArray{
				&databricks.FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArgs{
					VariantExprPath: pulumi.String("string"),
				},
			},
			Name: pulumi.String("string"),
			TimeseriesColumnIdentifier: &databricks.FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifierArgs{
				VariantExprPath: pulumi.String("string"),
			},
		},
	},
	Description:     pulumi.String("string"),
	FilterCondition: pulumi.String("string"),
	LineageContext: &databricks.FeatureEngineeringFeatureLineageContextArgs{
		JobContext: &databricks.FeatureEngineeringFeatureLineageContextJobContextArgs{
			JobId:    pulumi.Int(0),
			JobRunId: pulumi.Int(0),
		},
		NotebookId: pulumi.Int(0),
	},
	TimeWindow: &databricks.FeatureEngineeringFeatureTimeWindowArgs{
		Continuous: &databricks.FeatureEngineeringFeatureTimeWindowContinuousArgs{
			WindowDuration: pulumi.String("string"),
			Offset:         pulumi.String("string"),
		},
		Sliding: &databricks.FeatureEngineeringFeatureTimeWindowSlidingArgs{
			SlideDuration:  pulumi.String("string"),
			WindowDuration: pulumi.String("string"),
		},
		Tumbling: &databricks.FeatureEngineeringFeatureTimeWindowTumblingArgs{
			WindowDuration: pulumi.String("string"),
		},
	},
})

    var featureEngineeringFeatureResource = new FeatureEngineeringFeature("featureEngineeringFeatureResource", FeatureEngineeringFeatureArgs.builder()
    .fullName("string")
    .function(FeatureEngineeringFeatureFunctionArgs.builder()
        .functionType("string")
        .extraParameters(FeatureEngineeringFeatureFunctionExtraParameterArgs.builder()
            .key("string")
            .value("string")
            .build())
        .build())
    .inputs("string")
    .source(FeatureEngineeringFeatureSourceArgs.builder()
        .deltaTableSource(FeatureEngineeringFeatureSourceDeltaTableSourceArgs.builder()
            .entityColumns("string")
            .fullName("string")
            .timeseriesColumn("string")
            .build())
        .kafkaSource(FeatureEngineeringFeatureSourceKafkaSourceArgs.builder()
            .entityColumnIdentifiers(FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArgs.builder()
                .variantExprPath("string")
                .build())
            .name("string")
            .timeseriesColumnIdentifier(FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifierArgs.builder()
                .variantExprPath("string")
                .build())
            .build())
        .build())
    .description("string")
    .filterCondition("string")
    .lineageContext(FeatureEngineeringFeatureLineageContextArgs.builder()
        .jobContext(FeatureEngineeringFeatureLineageContextJobContextArgs.builder()
            .jobId(0)
            .jobRunId(0)
            .build())
        .notebookId(0)
        .build())
    .timeWindow(FeatureEngineeringFeatureTimeWindowArgs.builder()
        .continuous(FeatureEngineeringFeatureTimeWindowContinuousArgs.builder()
            .windowDuration("string")
            .offset("string")
            .build())
        .sliding(FeatureEngineeringFeatureTimeWindowSlidingArgs.builder()
            .slideDuration("string")
            .windowDuration("string")
            .build())
        .tumbling(FeatureEngineeringFeatureTimeWindowTumblingArgs.builder()
            .windowDuration("string")
            .build())
        .build())
    .build());

    feature_engineering_feature_resource = databricks.FeatureEngineeringFeature("featureEngineeringFeatureResource",
    full_name="string",
    function={
        "function_type": "string",
        "extra_parameters": [{
            "key": "string",
            "value": "string",
        }],
    },
    inputs=["string"],
    source={
        "delta_table_source": {
            "entity_columns": ["string"],
            "full_name": "string",
            "timeseries_column": "string",
        },
        "kafka_source": {
            "entity_column_identifiers": [{
                "variant_expr_path": "string",
            }],
            "name": "string",
            "timeseries_column_identifier": {
                "variant_expr_path": "string",
            },
        },
    },
    description="string",
    filter_condition="string",
    lineage_context={
        "job_context": {
            "job_id": 0,
            "job_run_id": 0,
        },
        "notebook_id": 0,
    },
    time_window={
        "continuous": {
            "window_duration": "string",
            "offset": "string",
        },
        "sliding": {
            "slide_duration": "string",
            "window_duration": "string",
        },
        "tumbling": {
            "window_duration": "string",
        },
    })

    const featureEngineeringFeatureResource = new databricks.FeatureEngineeringFeature("featureEngineeringFeatureResource", {
    fullName: "string",
    "function": {
        functionType: "string",
        extraParameters: [{
            key: "string",
            value: "string",
        }],
    },
    inputs: ["string"],
    source: {
        deltaTableSource: {
            entityColumns: ["string"],
            fullName: "string",
            timeseriesColumn: "string",
        },
        kafkaSource: {
            entityColumnIdentifiers: [{
                variantExprPath: "string",
            }],
            name: "string",
            timeseriesColumnIdentifier: {
                variantExprPath: "string",
            },
        },
    },
    description: "string",
    filterCondition: "string",
    lineageContext: {
        jobContext: {
            jobId: 0,
            jobRunId: 0,
        },
        notebookId: 0,
    },
    timeWindow: {
        continuous: {
            windowDuration: "string",
            offset: "string",
        },
        sliding: {
            slideDuration: "string",
            windowDuration: "string",
        },
        tumbling: {
            windowDuration: "string",
        },
    },
});

    type: databricks:FeatureEngineeringFeature
properties:
    description: string
    filterCondition: string
    fullName: string
    function:
        extraParameters:
            - key: string
              value: string
        functionType: string
    inputs:
        - string
    lineageContext:
        jobContext:
            jobId: 0
            jobRunId: 0
        notebookId: 0
    source:
        deltaTableSource:
            entityColumns:
                - string
            fullName: string
            timeseriesColumn: string
        kafkaSource:
            entityColumnIdentifiers:
                - variantExprPath: string
            name: string
            timeseriesColumnIdentifier:
                variantExprPath: string
    timeWindow:
        continuous:
            offset: string
            windowDuration: string
        sliding:
            slideDuration: string
            windowDuration: string
        tumbling:
            windowDuration: string

    FeatureEngineeringFeature Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The FeatureEngineeringFeature resource accepts the following input properties:

    FullName string
    The full three-part name (catalog, schema, name) of the feature
    Function FeatureEngineeringFeatureFunction
    The function by which the feature is computed
    Inputs List<string>
    The input columns from which the feature is computed
    Source FeatureEngineeringFeatureSource
    The data source of the feature
    Description string
    The description of the feature
    FilterCondition string
    The filter condition applied to the source data before aggregation
    LineageContext FeatureEngineeringFeatureLineageContext
    TimeWindow FeatureEngineeringFeatureTimeWindow
    The time window in which the feature is computed
    FullName string
    The full three-part name (catalog, schema, name) of the feature
    Function FeatureEngineeringFeatureFunctionArgs
    The function by which the feature is computed
    Inputs []string
    The input columns from which the feature is computed
    Source FeatureEngineeringFeatureSourceArgs
    The data source of the feature
    Description string
    The description of the feature
    FilterCondition string
    The filter condition applied to the source data before aggregation
    LineageContext FeatureEngineeringFeatureLineageContextArgs
    TimeWindow FeatureEngineeringFeatureTimeWindowArgs
    The time window in which the feature is computed
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunction
    The function by which the feature is computed
    inputs List<String>
    The input columns from which the feature is computed
    source FeatureEngineeringFeatureSource
    The data source of the feature
    description String
    The description of the feature
    filterCondition String
    The filter condition applied to the source data before aggregation
    lineageContext FeatureEngineeringFeatureLineageContext
    timeWindow FeatureEngineeringFeatureTimeWindow
    The time window in which the feature is computed
    fullName string
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunction
    The function by which the feature is computed
    inputs string[]
    The input columns from which the feature is computed
    source FeatureEngineeringFeatureSource
    The data source of the feature
    description string
    The description of the feature
    filterCondition string
    The filter condition applied to the source data before aggregation
    lineageContext FeatureEngineeringFeatureLineageContext
    timeWindow FeatureEngineeringFeatureTimeWindow
    The time window in which the feature is computed
    full_name str
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunctionArgs
    The function by which the feature is computed
    inputs Sequence[str]
    The input columns from which the feature is computed
    source FeatureEngineeringFeatureSourceArgs
    The data source of the feature
    description str
    The description of the feature
    filter_condition str
    The filter condition applied to the source data before aggregation
    lineage_context FeatureEngineeringFeatureLineageContextArgs
    time_window FeatureEngineeringFeatureTimeWindowArgs
    The time window in which the feature is computed
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    function Property Map
    The function by which the feature is computed
    inputs List<String>
    The input columns from which the feature is computed
    source Property Map
    The data source of the feature
    description String
    The description of the feature
    filterCondition String
    The filter condition applied to the source data before aggregation
    lineageContext Property Map
    timeWindow Property Map
    The time window in which the feature is computed

    Outputs

    All input properties are implicitly available as output properties. Additionally, the FeatureEngineeringFeature resource produces the following output properties:

    Id string
    The provider-assigned unique ID for this managed resource.
    Id string
    The provider-assigned unique ID for this managed resource.
    id String
    The provider-assigned unique ID for this managed resource.
    id string
    The provider-assigned unique ID for this managed resource.
    id str
    The provider-assigned unique ID for this managed resource.
    id String
    The provider-assigned unique ID for this managed resource.

    Look up Existing FeatureEngineeringFeature Resource

    Get an existing FeatureEngineeringFeature resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

    public static get(name: string, id: Input<ID>, state?: FeatureEngineeringFeatureState, opts?: CustomResourceOptions): FeatureEngineeringFeature
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        description: Optional[str] = None,
        filter_condition: Optional[str] = None,
        full_name: Optional[str] = None,
        function: Optional[FeatureEngineeringFeatureFunctionArgs] = None,
        inputs: Optional[Sequence[str]] = None,
        lineage_context: Optional[FeatureEngineeringFeatureLineageContextArgs] = None,
        source: Optional[FeatureEngineeringFeatureSourceArgs] = None,
        time_window: Optional[FeatureEngineeringFeatureTimeWindowArgs] = None) -> FeatureEngineeringFeature
    func GetFeatureEngineeringFeature(ctx *Context, name string, id IDInput, state *FeatureEngineeringFeatureState, opts ...ResourceOption) (*FeatureEngineeringFeature, error)
    public static FeatureEngineeringFeature Get(string name, Input<string> id, FeatureEngineeringFeatureState? state, CustomResourceOptions? opts = null)
    public static FeatureEngineeringFeature get(String name, Output<String> id, FeatureEngineeringFeatureState state, CustomResourceOptions options)
    resources:  _:    type: databricks:FeatureEngineeringFeature    get:      id: ${id}
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    resource_name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    The following state arguments are supported:
    Description string
    The description of the feature
    FilterCondition string
    The filter condition applied to the source data before aggregation
    FullName string
    The full three-part name (catalog, schema, name) of the feature
    Function FeatureEngineeringFeatureFunction
    The function by which the feature is computed
    Inputs List<string>
    The input columns from which the feature is computed
    LineageContext FeatureEngineeringFeatureLineageContext
    Source FeatureEngineeringFeatureSource
    The data source of the feature
    TimeWindow FeatureEngineeringFeatureTimeWindow
    The time window in which the feature is computed
    Description string
    The description of the feature
    FilterCondition string
    The filter condition applied to the source data before aggregation
    FullName string
    The full three-part name (catalog, schema, name) of the feature
    Function FeatureEngineeringFeatureFunctionArgs
    The function by which the feature is computed
    Inputs []string
    The input columns from which the feature is computed
    LineageContext FeatureEngineeringFeatureLineageContextArgs
    Source FeatureEngineeringFeatureSourceArgs
    The data source of the feature
    TimeWindow FeatureEngineeringFeatureTimeWindowArgs
    The time window in which the feature is computed
    description String
    The description of the feature
    filterCondition String
    The filter condition applied to the source data before aggregation
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunction
    The function by which the feature is computed
    inputs List<String>
    The input columns from which the feature is computed
    lineageContext FeatureEngineeringFeatureLineageContext
    source FeatureEngineeringFeatureSource
    The data source of the feature
    timeWindow FeatureEngineeringFeatureTimeWindow
    The time window in which the feature is computed
    description string
    The description of the feature
    filterCondition string
    The filter condition applied to the source data before aggregation
    fullName string
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunction
    The function by which the feature is computed
    inputs string[]
    The input columns from which the feature is computed
    lineageContext FeatureEngineeringFeatureLineageContext
    source FeatureEngineeringFeatureSource
    The data source of the feature
    timeWindow FeatureEngineeringFeatureTimeWindow
    The time window in which the feature is computed
    description str
    The description of the feature
    filter_condition str
    The filter condition applied to the source data before aggregation
    full_name str
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunctionArgs
    The function by which the feature is computed
    inputs Sequence[str]
    The input columns from which the feature is computed
    lineage_context FeatureEngineeringFeatureLineageContextArgs
    source FeatureEngineeringFeatureSourceArgs
    The data source of the feature
    time_window FeatureEngineeringFeatureTimeWindowArgs
    The time window in which the feature is computed
    description String
    The description of the feature
    filterCondition String
    The filter condition applied to the source data before aggregation
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    function Property Map
    The function by which the feature is computed
    inputs List<String>
    The input columns from which the feature is computed
    lineageContext Property Map
    source Property Map
    The data source of the feature
    timeWindow Property Map
    The time window in which the feature is computed

    Supporting Types

    FeatureEngineeringFeatureFunction, FeatureEngineeringFeatureFunctionArgs

    FunctionType string
    The type of the function. Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    ExtraParameters List<FeatureEngineeringFeatureFunctionExtraParameter>
    Extra parameters for parameterized functions
    FunctionType string
    The type of the function. Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    ExtraParameters []FeatureEngineeringFeatureFunctionExtraParameter
    Extra parameters for parameterized functions
    functionType String
    The type of the function. Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    extraParameters List<FeatureEngineeringFeatureFunctionExtraParameter>
    Extra parameters for parameterized functions
    functionType string
    The type of the function. Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    extraParameters FeatureEngineeringFeatureFunctionExtraParameter[]
    Extra parameters for parameterized functions
    function_type str
    The type of the function. Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    extra_parameters Sequence[FeatureEngineeringFeatureFunctionExtraParameter]
    Extra parameters for parameterized functions
    functionType String
    The type of the function. Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    extraParameters List<Property Map>
    Extra parameters for parameterized functions

    FeatureEngineeringFeatureFunctionExtraParameter, FeatureEngineeringFeatureFunctionExtraParameterArgs

    Key string
    The name of the parameter
    Value string
    The value of the parameter
    Key string
    The name of the parameter
    Value string
    The value of the parameter
    key String
    The name of the parameter
    value String
    The value of the parameter
    key string
    The name of the parameter
    value string
    The value of the parameter
    key str
    The name of the parameter
    value str
    The value of the parameter
    key String
    The name of the parameter
    value String
    The value of the parameter

    FeatureEngineeringFeatureLineageContext, FeatureEngineeringFeatureLineageContextArgs

    JobContext FeatureEngineeringFeatureLineageContextJobContext
    Job context information including job ID and run ID
    NotebookId int
    The notebook ID where this API was invoked
    JobContext FeatureEngineeringFeatureLineageContextJobContext
    Job context information including job ID and run ID
    NotebookId int
    The notebook ID where this API was invoked
    jobContext FeatureEngineeringFeatureLineageContextJobContext
    Job context information including job ID and run ID
    notebookId Integer
    The notebook ID where this API was invoked
    jobContext FeatureEngineeringFeatureLineageContextJobContext
    Job context information including job ID and run ID
    notebookId number
    The notebook ID where this API was invoked
    job_context FeatureEngineeringFeatureLineageContextJobContext
    Job context information including job ID and run ID
    notebook_id int
    The notebook ID where this API was invoked
    jobContext Property Map
    Job context information including job ID and run ID
    notebookId Number
    The notebook ID where this API was invoked

    FeatureEngineeringFeatureLineageContextJobContext, FeatureEngineeringFeatureLineageContextJobContextArgs

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

    FeatureEngineeringFeatureSource, FeatureEngineeringFeatureSourceArgs

    FeatureEngineeringFeatureSourceDeltaTableSource, FeatureEngineeringFeatureSourceDeltaTableSourceArgs

    EntityColumns List<string>
    The entity columns of the Delta table
    FullName string
    The full three-part name (catalog, schema, name) of the feature
    TimeseriesColumn string
    The timeseries column of the Delta table
    EntityColumns []string
    The entity columns of the Delta table
    FullName string
    The full three-part name (catalog, schema, name) of the feature
    TimeseriesColumn string
    The timeseries column of the Delta table
    entityColumns List<String>
    The entity columns of the Delta table
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    timeseriesColumn String
    The timeseries column of the Delta table
    entityColumns string[]
    The entity columns of the Delta table
    fullName string
    The full three-part name (catalog, schema, name) of the feature
    timeseriesColumn string
    The timeseries column of the Delta table
    entity_columns Sequence[str]
    The entity columns of the Delta table
    full_name str
    The full three-part name (catalog, schema, name) of the feature
    timeseries_column str
    The timeseries column of the Delta table
    entityColumns List<String>
    The entity columns of the Delta table
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    timeseriesColumn String
    The timeseries column of the Delta table

    FeatureEngineeringFeatureSourceKafkaSource, FeatureEngineeringFeatureSourceKafkaSourceArgs

    EntityColumnIdentifiers List<FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifier>
    The entity column identifiers of the Kafka source
    Name string
    Name of the Kafka source, used to identify it. This is used to look up the corresponding KafkaConfig object. Can be distinct from topic name
    TimeseriesColumnIdentifier FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifier
    The timeseries column identifier of the Kafka source
    EntityColumnIdentifiers []FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifier
    The entity column identifiers of the Kafka source
    Name string
    Name of the Kafka source, used to identify it. This is used to look up the corresponding KafkaConfig object. Can be distinct from topic name
    TimeseriesColumnIdentifier FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifier
    The timeseries column identifier of the Kafka source
    entityColumnIdentifiers List<FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifier>
    The entity column identifiers of the Kafka source
    name String
    Name of the Kafka source, used to identify it. This is used to look up the corresponding KafkaConfig object. Can be distinct from topic name
    timeseriesColumnIdentifier FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifier
    The timeseries column identifier of the Kafka source
    entityColumnIdentifiers FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifier[]
    The entity column identifiers of the Kafka source
    name string
    Name of the Kafka source, used to identify it. This is used to look up the corresponding KafkaConfig object. Can be distinct from topic name
    timeseriesColumnIdentifier FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifier
    The timeseries column identifier of the Kafka source
    entity_column_identifiers Sequence[FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifier]
    The entity column identifiers of the Kafka source
    name str
    Name of the Kafka source, used to identify it. This is used to look up the corresponding KafkaConfig object. Can be distinct from topic name
    timeseries_column_identifier FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifier
    The timeseries column identifier of the Kafka source
    entityColumnIdentifiers List<Property Map>
    The entity column identifiers of the Kafka source
    name String
    Name of the Kafka source, used to identify it. This is used to look up the corresponding KafkaConfig object. Can be distinct from topic name
    timeseriesColumnIdentifier Property Map
    The timeseries column identifier of the Kafka source

    FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifier, FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArgs

    VariantExprPath string
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    VariantExprPath string
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    variantExprPath String
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    variantExprPath string
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    variant_expr_path str
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    variantExprPath String
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip

    FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifier, FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifierArgs

    VariantExprPath string
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    VariantExprPath string
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    variantExprPath String
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    variantExprPath string
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    variant_expr_path str
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip
    variantExprPath String
    String representation of the column name or variant expression path. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value:trip_details.location_details.pickup_zip is pickup_zip

    FeatureEngineeringFeatureTimeWindow, FeatureEngineeringFeatureTimeWindowArgs

    FeatureEngineeringFeatureTimeWindowContinuous, FeatureEngineeringFeatureTimeWindowContinuousArgs

    WindowDuration string
    Offset string
    The offset of the continuous window (must be non-positive)
    WindowDuration string
    Offset string
    The offset of the continuous window (must be non-positive)
    windowDuration String
    offset String
    The offset of the continuous window (must be non-positive)
    windowDuration string
    offset string
    The offset of the continuous window (must be non-positive)
    window_duration str
    offset str
    The offset of the continuous window (must be non-positive)
    windowDuration String
    offset String
    The offset of the continuous window (must be non-positive)

    FeatureEngineeringFeatureTimeWindowSliding, FeatureEngineeringFeatureTimeWindowSlidingArgs

    SlideDuration string
    The slide duration (interval by which windows advance, must be positive and less than duration)
    WindowDuration string
    SlideDuration string
    The slide duration (interval by which windows advance, must be positive and less than duration)
    WindowDuration string
    slideDuration String
    The slide duration (interval by which windows advance, must be positive and less than duration)
    windowDuration String
    slideDuration string
    The slide duration (interval by which windows advance, must be positive and less than duration)
    windowDuration string
    slide_duration str
    The slide duration (interval by which windows advance, must be positive and less than duration)
    window_duration str
    slideDuration String
    The slide duration (interval by which windows advance, must be positive and less than duration)
    windowDuration String

    FeatureEngineeringFeatureTimeWindowTumbling, FeatureEngineeringFeatureTimeWindowTumblingArgs

    Import

    As of Pulumi v1.5, resources can be imported through configuration.

    hcl

    import {

    id = “full_name”

    to = databricks_feature_engineering_feature.this

    }

    If you are using an older version of Pulumi, import the resource using the pulumi import command as follows:

    $ pulumi import databricks:index/featureEngineeringFeature:FeatureEngineeringFeature this "full_name"

    To learn more about importing existing cloud resources, see Importing resources.

    Package Details

    Repository
    databricks pulumi/pulumi-databricks
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the databricks Terraform Provider.
    databricks logo
    Databricks v1.83.0 published on Friday, Jan 23, 2026 by Pulumi
    Schema (JSON)
    pulumi/pulumi-databricks
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