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Viewing docs for Databricks v1.118.1
published on Tuesday, Oct 27, 2026 by Pulumi
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pulumi/pulumi-databricks

databricks.FeatureEngineeringFeature

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Viewing docs for Databricks v1.118.1
published on Tuesday, Oct 27, 2026 by Pulumi
Schema (JSON)
pulumi/pulumi-databricks

    GA

    API Documentation

    A feature is a reusable, governed definition of a model input registered in Unity Catalog. Each feature binds a data source to the function that computes its value, so model training and online serving read one definition instead of two implementations that drift apart.

    Registering a feature computes nothing on its own. To produce values, materialize the feature to an offline Delta table or an online Lakebase table using the Feature Engineering API.

    Only description can be changed in place. Changing any other field replaces the feature.

    Example Usage

    A feature needs a source and a function. A Delta table source also requires the entity columns that key the feature and a timeseries column; a request source takes neither, because the value arrives in the request.

    Aggregation over a sliding window

    Count each customer’s transactions over the trailing seven days, recomputed daily.

    import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";

const transactionCount7d = new databricks.FeatureEngineeringFeature("transaction_count_7d", {
    fullName: "main.features.transaction_count_7d",
    description: "Transactions per customer over the trailing seven days.",
    source: {
        deltaTableSource: {
            fullName: "main.retail.transactions",
        },
    },
    "function": {
        aggregationFunction: {
            countFunction: {
                input: "transaction_id",
            },
            timeWindow: {
                sliding: {
                    windowDuration: "604800s",
                    slideDuration: "86400s",
                },
            },
        },
    },
    entities: [{
        name: "customer_id",
    }],
    timeseriesColumn: {
        name: "transaction_time",
    },
});

    import pulumi
import pulumi_databricks as databricks

transaction_count7d = databricks.FeatureEngineeringFeature("transaction_count_7d",
    full_name="main.features.transaction_count_7d",
    description="Transactions per customer over the trailing seven days.",
    source={
        "delta_table_source": {
            "full_name": "main.retail.transactions",
        },
    },
    function={
        "aggregation_function": {
            "count_function": {
                "input": "transaction_id",
            },
            "time_window": {
                "sliding": {
                    "window_duration": "604800s",
                    "slide_duration": "86400s",
                },
            },
        },
    },
    entities=[{
        "name": "customer_id",
    }],
    timeseries_column={
        "name": "transaction_time",
    })

    package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewFeatureEngineeringFeature(ctx, "transaction_count_7d", &databricks.FeatureEngineeringFeatureArgs{
			FullName:    pulumi.String("main.features.transaction_count_7d"),
			Description: pulumi.String("Transactions per customer over the trailing seven days."),
			Source: &databricks.FeatureEngineeringFeatureSourceArgs{
				DeltaTableSource: &databricks.FeatureEngineeringFeatureSourceDeltaTableSourceArgs{
					FullName: pulumi.String("main.retail.transactions"),
				},
			},
			Function: &databricks.FeatureEngineeringFeatureFunctionArgs{
				AggregationFunction: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionArgs{
					CountFunction: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionCountFunctionArgs{
						Input: pulumi.String("transaction_id"),
					},
					TimeWindow: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowArgs{
						Sliding: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSlidingArgs{
							WindowDuration: pulumi.String("604800s"),
							SlideDuration:  pulumi.String("86400s"),
						},
					},
				},
			},
			Entities: databricks.FeatureEngineeringFeatureEntityArray{
				&databricks.FeatureEngineeringFeatureEntityArgs{
					Name: pulumi.String("customer_id"),
				},
			},
			TimeseriesColumn: &databricks.FeatureEngineeringFeatureTimeseriesColumnArgs{
				Name: pulumi.String("transaction_time"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var transactionCount7d = new Databricks.FeatureEngineeringFeature("transaction_count_7d", new()
    {
        FullName = "main.features.transaction_count_7d",
        Description = "Transactions per customer over the trailing seven days.",
        Source = new Databricks.Inputs.FeatureEngineeringFeatureSourceArgs
        {
            DeltaTableSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceDeltaTableSourceArgs
            {
                FullName = "main.retail.transactions",
            },
        },
        Function = new Databricks.Inputs.FeatureEngineeringFeatureFunctionArgs
        {
            AggregationFunction = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionArgs
            {
                CountFunction = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionCountFunctionArgs
                {
                    Input = "transaction_id",
                },
                TimeWindow = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowArgs
                {
                    Sliding = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSlidingArgs
                    {
                        WindowDuration = "604800s",
                        SlideDuration = "86400s",
                    },
                },
            },
        },
        Entities = new[]
        {
            new Databricks.Inputs.FeatureEngineeringFeatureEntityArgs
            {
                Name = "customer_id",
            },
        },
        TimeseriesColumn = new Databricks.Inputs.FeatureEngineeringFeatureTimeseriesColumnArgs
        {
            Name = "transaction_time",
        },
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.FeatureEngineeringFeature;
import com.pulumi.databricks.FeatureEngineeringFeatureArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureSourceArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureSourceDeltaTableSourceArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureFunctionArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureFunctionAggregationFunctionArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureFunctionAggregationFunctionCountFunctionArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSlidingArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureEntityArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureTimeseriesColumnArgs;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var transactionCount7d = new FeatureEngineeringFeature("transactionCount7d", FeatureEngineeringFeatureArgs.builder()
            .fullName("main.features.transaction_count_7d")
            .description("Transactions per customer over the trailing seven days.")
            .source(FeatureEngineeringFeatureSourceArgs.builder()
                .deltaTableSource(FeatureEngineeringFeatureSourceDeltaTableSourceArgs.builder()
                    .fullName("main.retail.transactions")
                    .build())
                .build())
            .function(FeatureEngineeringFeatureFunctionArgs.builder()
                .aggregationFunction(FeatureEngineeringFeatureFunctionAggregationFunctionArgs.builder()
                    .countFunction(FeatureEngineeringFeatureFunctionAggregationFunctionCountFunctionArgs.builder()
                        .input("transaction_id")
                        .build())
                    .timeWindow(FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowArgs.builder()
                        .sliding(FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSlidingArgs.builder()
                            .windowDuration("604800s")
                            .slideDuration("86400s")
                            .build())
                        .build())
                    .build())
                .build())
            .entities(FeatureEngineeringFeatureEntityArgs.builder()
                .name("customer_id")
                .build())
            .timeseriesColumn(FeatureEngineeringFeatureTimeseriesColumnArgs.builder()
                .name("transaction_time")
                .build())
            .build());

    }
}

    resources:
  transactionCount7d:
    type: databricks:FeatureEngineeringFeature
    name: transaction_count_7d
    properties:
      fullName: main.features.transaction_count_7d
      description: Transactions per customer over the trailing seven days.
      source:
        deltaTableSource:
          fullName: main.retail.transactions
      function:
        aggregationFunction:
          countFunction:
            input: transaction_id
          timeWindow:
            sliding:
              windowDuration: 604800s
              slideDuration: 86400s
      entities:
        - name: customer_id
      timeseriesColumn:
        name: transaction_time

    terraform {
  required_providers {
    databricks = {
      source = "pulumi/databricks"
    }
  }
}

resource "databricks_feature_engineering_feature" "transaction_count_7d" {
  lifecycle {
    create_before_destroy = true
  }
  full_name   = "main.features.transaction_count_7d"
  description = "Transactions per customer over the trailing seven days."
  source = {
    delta_table_source = {
      full_name = "main.retail.transactions"
    }
  }
  function = {
    aggregation_function = {
      count_function = {
        input = "transaction_id"
      }
      time_window = {
        sliding = {
          window_duration = "604800s"
          slide_duration  = "86400s"
        }
      }
    }
  }
  entities = [{
    name = "customer_id"
  }]
  timeseries_column = {
    name = "transaction_time"
  }
}

    Latest value of a column

    Read a column straight from the source with no aggregation, so no time window is needed. The timeseries column is still required, because it is what makes the lookup point-in-time correct.

    import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";

const customerTier = new databricks.FeatureEngineeringFeature("customer_tier", {
    fullName: "main.features.customer_tier",
    source: {
        deltaTableSource: {
            fullName: "main.retail.customers",
        },
    },
    "function": {
        columnSelection: {
            column: "tier",
        },
    },
    entities: [{
        name: "customer_id",
    }],
    timeseriesColumn: {
        name: "updated_at",
    },
});

    import pulumi
import pulumi_databricks as databricks

customer_tier = databricks.FeatureEngineeringFeature("customer_tier",
    full_name="main.features.customer_tier",
    source={
        "delta_table_source": {
            "full_name": "main.retail.customers",
        },
    },
    function={
        "column_selection": {
            "column": "tier",
        },
    },
    entities=[{
        "name": "customer_id",
    }],
    timeseries_column={
        "name": "updated_at",
    })

    package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewFeatureEngineeringFeature(ctx, "customer_tier", &databricks.FeatureEngineeringFeatureArgs{
			FullName: pulumi.String("main.features.customer_tier"),
			Source: &databricks.FeatureEngineeringFeatureSourceArgs{
				DeltaTableSource: &databricks.FeatureEngineeringFeatureSourceDeltaTableSourceArgs{
					FullName: pulumi.String("main.retail.customers"),
				},
			},
			Function: &databricks.FeatureEngineeringFeatureFunctionArgs{
				ColumnSelection: &databricks.FeatureEngineeringFeatureFunctionColumnSelectionArgs{
					Column: pulumi.String("tier"),
				},
			},
			Entities: databricks.FeatureEngineeringFeatureEntityArray{
				&databricks.FeatureEngineeringFeatureEntityArgs{
					Name: pulumi.String("customer_id"),
				},
			},
			TimeseriesColumn: &databricks.FeatureEngineeringFeatureTimeseriesColumnArgs{
				Name: pulumi.String("updated_at"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var customerTier = new Databricks.FeatureEngineeringFeature("customer_tier", new()
    {
        FullName = "main.features.customer_tier",
        Source = new Databricks.Inputs.FeatureEngineeringFeatureSourceArgs
        {
            DeltaTableSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceDeltaTableSourceArgs
            {
                FullName = "main.retail.customers",
            },
        },
        Function = new Databricks.Inputs.FeatureEngineeringFeatureFunctionArgs
        {
            ColumnSelection = new Databricks.Inputs.FeatureEngineeringFeatureFunctionColumnSelectionArgs
            {
                Column = "tier",
            },
        },
        Entities = new[]
        {
            new Databricks.Inputs.FeatureEngineeringFeatureEntityArgs
            {
                Name = "customer_id",
            },
        },
        TimeseriesColumn = new Databricks.Inputs.FeatureEngineeringFeatureTimeseriesColumnArgs
        {
            Name = "updated_at",
        },
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.FeatureEngineeringFeature;
import com.pulumi.databricks.FeatureEngineeringFeatureArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureSourceArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureSourceDeltaTableSourceArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureFunctionArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureFunctionColumnSelectionArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureEntityArgs;
import com.pulumi.databricks.inputs.FeatureEngineeringFeatureTimeseriesColumnArgs;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var customerTier = new FeatureEngineeringFeature("customerTier", FeatureEngineeringFeatureArgs.builder()
            .fullName("main.features.customer_tier")
            .source(FeatureEngineeringFeatureSourceArgs.builder()
                .deltaTableSource(FeatureEngineeringFeatureSourceDeltaTableSourceArgs.builder()
                    .fullName("main.retail.customers")
                    .build())
                .build())
            .function(FeatureEngineeringFeatureFunctionArgs.builder()
                .columnSelection(FeatureEngineeringFeatureFunctionColumnSelectionArgs.builder()
                    .column("tier")
                    .build())
                .build())
            .entities(FeatureEngineeringFeatureEntityArgs.builder()
                .name("customer_id")
                .build())
            .timeseriesColumn(FeatureEngineeringFeatureTimeseriesColumnArgs.builder()
                .name("updated_at")
                .build())
            .build());

    }
}

    resources:
  customerTier:
    type: databricks:FeatureEngineeringFeature
    name: customer_tier
    properties:
      fullName: main.features.customer_tier
      source:
        deltaTableSource:
          fullName: main.retail.customers
      function:
        columnSelection:
          column: tier
      entities:
        - name: customer_id
      timeseriesColumn:
        name: updated_at

    terraform {
  required_providers {
    databricks = {
      source = "pulumi/databricks"
    }
  }
}

resource "databricks_feature_engineering_feature" "customer_tier" {
  lifecycle {
    create_before_destroy = true
  }
  full_name = "main.features.customer_tier"
  source = {
    delta_table_source = {
      full_name = "main.retail.customers"
    }
  }
  function = {
    column_selection = {
      column = "tier"
    }
  }
  entities = [{
    name = "customer_id"
  }]
  timeseries_column = {
    name = "updated_at"
  }
}

    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,
                              source: Optional[FeatureEngineeringFeatureSourceArgs] = None,
                              description: Optional[str] = None,
                              entities: Optional[Sequence[FeatureEngineeringFeatureEntityArgs]] = None,
                              filter_condition: Optional[str] = None,
                              inputs: Optional[Sequence[str]] = None,
                              lineage_context: Optional[FeatureEngineeringFeatureLineageContextArgs] = None,
                              provider_config: Optional[FeatureEngineeringFeatureProviderConfigArgs] = None,
                              time_window: Optional[FeatureEngineeringFeatureTimeWindowArgs] = None,
                              timeseries_column: Optional[FeatureEngineeringFeatureTimeseriesColumnArgs] = 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.


    resource "databricks_feature_engineering_feature" "name" {
    # resource properties
}

    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
    {
        AggregationFunction = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionArgs
        {
            ApproxCountDistinct = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinctArgs
            {
                Input = "string",
                RelativeSd = 0.0,
            },
            ApproxPercentile = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentileArgs
            {
                Input = "string",
                Percentile = 0.0,
                Accuracy = 0,
            },
            Avg = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionAvgArgs
            {
                Input = "string",
            },
            CountFunction = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionCountFunctionArgs
            {
                Input = "string",
            },
            First = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionFirstArgs
            {
                Input = "string",
            },
            FirstDistinct = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinctArgs
            {
                Input = "string",
                N = 0,
            },
            FirstN = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionFirstNArgs
            {
                Input = "string",
                N = 0,
            },
            Last = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionLastArgs
            {
                Input = "string",
            },
            LastDistinct = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinctArgs
            {
                Input = "string",
                N = 0,
            },
            LastN = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionLastNArgs
            {
                Input = "string",
                N = 0,
            },
            Max = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionMaxArgs
            {
                Input = "string",
            },
            Min = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionMinArgs
            {
                Input = "string",
            },
            StddevPop = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionStddevPopArgs
            {
                Input = "string",
            },
            StddevSamp = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionStddevSampArgs
            {
                Input = "string",
            },
            Sum = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionSumArgs
            {
                Input = "string",
            },
            TimeWindow = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowArgs
            {
                Continuous = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuousArgs
                {
                    WindowDuration = "string",
                    Offset = "string",
                },
                Rolling = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRollingArgs
                {
                    Delay = "string",
                    WindowDuration = "string",
                },
                Sawtooth = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtoothArgs
                {
                    Delay = "string",
                    WindowDuration = "string",
                },
                Sliding = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSlidingArgs
                {
                    SlideDuration = "string",
                    Delay = "string",
                    Offset = "string",
                    WindowDuration = "string",
                },
                StartTime = "string",
                Tumbling = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumblingArgs
                {
                    WindowDuration = "string",
                    Delay = "string",
                    Offset = "string",
                },
            },
            VarPop = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionVarPopArgs
            {
                Input = "string",
            },
            VarSamp = new Databricks.Inputs.FeatureEngineeringFeatureFunctionAggregationFunctionVarSampArgs
            {
                Input = "string",
            },
        },
        ColumnSelection = new Databricks.Inputs.FeatureEngineeringFeatureFunctionColumnSelectionArgs
        {
            Column = "string",
        },
        CustomUdf = new Databricks.Inputs.FeatureEngineeringFeatureFunctionCustomUdfArgs
        {
            FunctionPath = "string",
            InputBindings = new[]
            {
                new Databricks.Inputs.FeatureEngineeringFeatureFunctionCustomUdfInputBindingArgs
                {
                    Column = "string",
                    Parameter = "string",
                },
            },
        },
        ExtraParameters = new[]
        {
            new Databricks.Inputs.FeatureEngineeringFeatureFunctionExtraParameterArgs
            {
                Key = "string",
                Value = "string",
            },
        },
        FunctionType = "string",
    },
    Source = new Databricks.Inputs.FeatureEngineeringFeatureSourceArgs
    {
        DeltaTableSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceDeltaTableSourceArgs
        {
            FullName = "string",
            DataframeSchema = "string",
            EntityColumns = new[]
            {
                "string",
            },
            FilterCondition = "string",
            TimeseriesColumn = "string",
            TransformationSql = "string",
        },
        FeatureViewSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceFeatureViewSourceArgs
        {
            FeatureReferences = new[]
            {
                new Databricks.Inputs.FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReferenceArgs
                {
                    Feature = "string",
                },
            },
        },
        KafkaSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceKafkaSourceArgs
        {
            Name = "string",
            EntityColumnIdentifiers = new[]
            {
                new Databricks.Inputs.FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArgs
                {
                    VariantExprPath = "string",
                },
            },
            FilterCondition = "string",
            TimeseriesColumnIdentifier = new Databricks.Inputs.FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifierArgs
            {
                VariantExprPath = "string",
            },
        },
        Lateness = new Databricks.Inputs.FeatureEngineeringFeatureSourceLatenessArgs
        {
            SettlingDelay = "string",
        },
        RequestSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceRequestSourceArgs
        {
            DataframeSchema = "string",
            FlatSchema = new Databricks.Inputs.FeatureEngineeringFeatureSourceRequestSourceFlatSchemaArgs
            {
                Fields = new[]
                {
                    new Databricks.Inputs.FeatureEngineeringFeatureSourceRequestSourceFlatSchemaFieldArgs
                    {
                        DataType = "string",
                        Name = "string",
                    },
                },
            },
        },
        SdpSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceSdpSourceArgs
        {
            DataframeSchema = "string",
            FilterCondition = "string",
            InputTables = new[]
            {
                "string",
            },
            OutputTable = "string",
            PipelineId = "string",
            PipelineState = "string",
            TransformationSql = "string",
        },
        StreamSource = new Databricks.Inputs.FeatureEngineeringFeatureSourceStreamSourceArgs
        {
            FullName = "string",
            DataframeSchema = "string",
            FilterCondition = "string",
            TransformationSql = "string",
        },
    },
    Description = "string",
    Entities = new[]
    {
        new Databricks.Inputs.FeatureEngineeringFeatureEntityArgs
        {
            Name = "string",
        },
    },
    FilterCondition = "string",
    Inputs = new[]
    {
        "string",
    },
    LineageContext = new Databricks.Inputs.FeatureEngineeringFeatureLineageContextArgs
    {
        JobContext = new Databricks.Inputs.FeatureEngineeringFeatureLineageContextJobContextArgs
        {
            JobId = 0,
            JobRunId = 0,
        },
        NotebookId = 0,
    },
    ProviderConfig = new Databricks.Inputs.FeatureEngineeringFeatureProviderConfigArgs
    {
        WorkspaceId = "string",
    },
    TimeWindow = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowArgs
    {
        Continuous = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowContinuousArgs
        {
            WindowDuration = "string",
            Offset = "string",
        },
        Rolling = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowRollingArgs
        {
            Delay = "string",
            WindowDuration = "string",
        },
        Sawtooth = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowSawtoothArgs
        {
            Delay = "string",
            WindowDuration = "string",
        },
        Sliding = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowSlidingArgs
        {
            SlideDuration = "string",
            Delay = "string",
            Offset = "string",
            WindowDuration = "string",
        },
        StartTime = "string",
        Tumbling = new Databricks.Inputs.FeatureEngineeringFeatureTimeWindowTumblingArgs
        {
            WindowDuration = "string",
            Delay = "string",
            Offset = "string",
        },
    },
    TimeseriesColumn = new Databricks.Inputs.FeatureEngineeringFeatureTimeseriesColumnArgs
    {
        Name = "string",
    },
});

    example, err := databricks.NewFeatureEngineeringFeature(ctx, "featureEngineeringFeatureResource", &databricks.FeatureEngineeringFeatureArgs{
	FullName: pulumi.String("string"),
	Function: &databricks.FeatureEngineeringFeatureFunctionArgs{
		AggregationFunction: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionArgs{
			ApproxCountDistinct: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinctArgs{
				Input:      pulumi.String("string"),
				RelativeSd: pulumi.Float64(0),
			},
			ApproxPercentile: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentileArgs{
				Input:      pulumi.String("string"),
				Percentile: pulumi.Float64(0),
				Accuracy:   pulumi.Int(0),
			},
			Avg: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionAvgArgs{
				Input: pulumi.String("string"),
			},
			CountFunction: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionCountFunctionArgs{
				Input: pulumi.String("string"),
			},
			First: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionFirstArgs{
				Input: pulumi.String("string"),
			},
			FirstDistinct: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinctArgs{
				Input: pulumi.String("string"),
				N:     pulumi.Int(0),
			},
			FirstN: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionFirstNArgs{
				Input: pulumi.String("string"),
				N:     pulumi.Int(0),
			},
			Last: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionLastArgs{
				Input: pulumi.String("string"),
			},
			LastDistinct: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinctArgs{
				Input: pulumi.String("string"),
				N:     pulumi.Int(0),
			},
			LastN: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionLastNArgs{
				Input: pulumi.String("string"),
				N:     pulumi.Int(0),
			},
			Max: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionMaxArgs{
				Input: pulumi.String("string"),
			},
			Min: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionMinArgs{
				Input: pulumi.String("string"),
			},
			StddevPop: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionStddevPopArgs{
				Input: pulumi.String("string"),
			},
			StddevSamp: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionStddevSampArgs{
				Input: pulumi.String("string"),
			},
			Sum: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionSumArgs{
				Input: pulumi.String("string"),
			},
			TimeWindow: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowArgs{
				Continuous: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuousArgs{
					WindowDuration: pulumi.String("string"),
					Offset:         pulumi.String("string"),
				},
				Rolling: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRollingArgs{
					Delay:          pulumi.String("string"),
					WindowDuration: pulumi.String("string"),
				},
				Sawtooth: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtoothArgs{
					Delay:          pulumi.String("string"),
					WindowDuration: pulumi.String("string"),
				},
				Sliding: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSlidingArgs{
					SlideDuration:  pulumi.String("string"),
					Delay:          pulumi.String("string"),
					Offset:         pulumi.String("string"),
					WindowDuration: pulumi.String("string"),
				},
				StartTime: pulumi.String("string"),
				Tumbling: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumblingArgs{
					WindowDuration: pulumi.String("string"),
					Delay:          pulumi.String("string"),
					Offset:         pulumi.String("string"),
				},
			},
			VarPop: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionVarPopArgs{
				Input: pulumi.String("string"),
			},
			VarSamp: &databricks.FeatureEngineeringFeatureFunctionAggregationFunctionVarSampArgs{
				Input: pulumi.String("string"),
			},
		},
		ColumnSelection: &databricks.FeatureEngineeringFeatureFunctionColumnSelectionArgs{
			Column: pulumi.String("string"),
		},
		CustomUdf: &databricks.FeatureEngineeringFeatureFunctionCustomUdfArgs{
			FunctionPath: pulumi.String("string"),
			InputBindings: databricks.FeatureEngineeringFeatureFunctionCustomUdfInputBindingArray{
				&databricks.FeatureEngineeringFeatureFunctionCustomUdfInputBindingArgs{
					Column:    pulumi.String("string"),
					Parameter: pulumi.String("string"),
				},
			},
		},
		ExtraParameters: databricks.FeatureEngineeringFeatureFunctionExtraParameterArray{
			&databricks.FeatureEngineeringFeatureFunctionExtraParameterArgs{
				Key:   pulumi.String("string"),
				Value: pulumi.String("string"),
			},
		},
		FunctionType: pulumi.String("string"),
	},
	Source: &databricks.FeatureEngineeringFeatureSourceArgs{
		DeltaTableSource: &databricks.FeatureEngineeringFeatureSourceDeltaTableSourceArgs{
			FullName:        pulumi.String("string"),
			DataframeSchema: pulumi.String("string"),
			EntityColumns: pulumi.StringArray{
				pulumi.String("string"),
			},
			FilterCondition:   pulumi.String("string"),
			TimeseriesColumn:  pulumi.String("string"),
			TransformationSql: pulumi.String("string"),
		},
		FeatureViewSource: &databricks.FeatureEngineeringFeatureSourceFeatureViewSourceArgs{
			FeatureReferences: databricks.FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReferenceArray{
				&databricks.FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReferenceArgs{
					Feature: pulumi.String("string"),
				},
			},
		},
		KafkaSource: &databricks.FeatureEngineeringFeatureSourceKafkaSourceArgs{
			Name: pulumi.String("string"),
			EntityColumnIdentifiers: databricks.FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArray{
				&databricks.FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArgs{
					VariantExprPath: pulumi.String("string"),
				},
			},
			FilterCondition: pulumi.String("string"),
			TimeseriesColumnIdentifier: &databricks.FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifierArgs{
				VariantExprPath: pulumi.String("string"),
			},
		},
		Lateness: &databricks.FeatureEngineeringFeatureSourceLatenessArgs{
			SettlingDelay: pulumi.String("string"),
		},
		RequestSource: &databricks.FeatureEngineeringFeatureSourceRequestSourceArgs{
			DataframeSchema: pulumi.String("string"),
			FlatSchema: &databricks.FeatureEngineeringFeatureSourceRequestSourceFlatSchemaArgs{
				Fields: databricks.FeatureEngineeringFeatureSourceRequestSourceFlatSchemaFieldArray{
					&databricks.FeatureEngineeringFeatureSourceRequestSourceFlatSchemaFieldArgs{
						DataType: pulumi.String("string"),
						Name:     pulumi.String("string"),
					},
				},
			},
		},
		SdpSource: &databricks.FeatureEngineeringFeatureSourceSdpSourceArgs{
			DataframeSchema: pulumi.String("string"),
			FilterCondition: pulumi.String("string"),
			InputTables: pulumi.StringArray{
				pulumi.String("string"),
			},
			OutputTable:       pulumi.String("string"),
			PipelineId:        pulumi.String("string"),
			PipelineState:     pulumi.String("string"),
			TransformationSql: pulumi.String("string"),
		},
		StreamSource: &databricks.FeatureEngineeringFeatureSourceStreamSourceArgs{
			FullName:          pulumi.String("string"),
			DataframeSchema:   pulumi.String("string"),
			FilterCondition:   pulumi.String("string"),
			TransformationSql: pulumi.String("string"),
		},
	},
	Description: pulumi.String("string"),
	Entities: databricks.FeatureEngineeringFeatureEntityArray{
		&databricks.FeatureEngineeringFeatureEntityArgs{
			Name: pulumi.String("string"),
		},
	},
	FilterCondition: pulumi.String("string"),
	Inputs: pulumi.StringArray{
		pulumi.String("string"),
	},
	LineageContext: &databricks.FeatureEngineeringFeatureLineageContextArgs{
		JobContext: &databricks.FeatureEngineeringFeatureLineageContextJobContextArgs{
			JobId:    pulumi.Int(0),
			JobRunId: pulumi.Int(0),
		},
		NotebookId: pulumi.Int(0),
	},
	ProviderConfig: &databricks.FeatureEngineeringFeatureProviderConfigArgs{
		WorkspaceId: pulumi.String("string"),
	},
	TimeWindow: &databricks.FeatureEngineeringFeatureTimeWindowArgs{
		Continuous: &databricks.FeatureEngineeringFeatureTimeWindowContinuousArgs{
			WindowDuration: pulumi.String("string"),
			Offset:         pulumi.String("string"),
		},
		Rolling: &databricks.FeatureEngineeringFeatureTimeWindowRollingArgs{
			Delay:          pulumi.String("string"),
			WindowDuration: pulumi.String("string"),
		},
		Sawtooth: &databricks.FeatureEngineeringFeatureTimeWindowSawtoothArgs{
			Delay:          pulumi.String("string"),
			WindowDuration: pulumi.String("string"),
		},
		Sliding: &databricks.FeatureEngineeringFeatureTimeWindowSlidingArgs{
			SlideDuration:  pulumi.String("string"),
			Delay:          pulumi.String("string"),
			Offset:         pulumi.String("string"),
			WindowDuration: pulumi.String("string"),
		},
		StartTime: pulumi.String("string"),
		Tumbling: &databricks.FeatureEngineeringFeatureTimeWindowTumblingArgs{
			WindowDuration: pulumi.String("string"),
			Delay:          pulumi.String("string"),
			Offset:         pulumi.String("string"),
		},
	},
	TimeseriesColumn: &databricks.FeatureEngineeringFeatureTimeseriesColumnArgs{
		Name: pulumi.String("string"),
	},
})

    resource "databricks_feature_engineering_feature" "featureEngineeringFeatureResource" {
  lifecycle {
    create_before_destroy = true
  }
  full_name = "string"
  function = {
    aggregation_function = {
      approx_count_distinct = {
        input       = "string"
        relative_sd = 0
      }
      approx_percentile = {
        input      = "string"
        percentile = 0
        accuracy   = 0
      }
      avg = {
        input = "string"
      }
      count_function = {
        input = "string"
      }
      first = {
        input = "string"
      }
      first_distinct = {
        input = "string"
        n     = 0
      }
      first_n = {
        input = "string"
        n     = 0
      }
      last = {
        input = "string"
      }
      last_distinct = {
        input = "string"
        n     = 0
      }
      last_n = {
        input = "string"
        n     = 0
      }
      max = {
        input = "string"
      }
      min = {
        input = "string"
      }
      stddev_pop = {
        input = "string"
      }
      stddev_samp = {
        input = "string"
      }
      sum = {
        input = "string"
      }
      time_window = {
        continuous = {
          window_duration = "string"
          offset          = "string"
        }
        rolling = {
          delay           = "string"
          window_duration = "string"
        }
        sawtooth = {
          delay           = "string"
          window_duration = "string"
        }
        sliding = {
          slide_duration  = "string"
          delay           = "string"
          offset          = "string"
          window_duration = "string"
        }
        start_time = "string"
        tumbling = {
          window_duration = "string"
          delay           = "string"
          offset          = "string"
        }
      }
      var_pop = {
        input = "string"
      }
      var_samp = {
        input = "string"
      }
    }
    column_selection = {
      column = "string"
    }
    custom_udf = {
      function_path = "string"
      input_bindings = [{
        column    = "string"
        parameter = "string"
      }]
    }
    extra_parameters = [{
      key   = "string"
      value = "string"
    }]
    function_type = "string"
  }
  source = {
    delta_table_source = {
      full_name          = "string"
      dataframe_schema   = "string"
      entity_columns     = ["string"]
      filter_condition   = "string"
      timeseries_column  = "string"
      transformation_sql = "string"
    }
    feature_view_source = {
      feature_references = [{
        feature = "string"
      }]
    }
    kafka_source = {
      name = "string"
      entity_column_identifiers = [{
        variant_expr_path = "string"
      }]
      filter_condition = "string"
      timeseries_column_identifier = {
        variant_expr_path = "string"
      }
    }
    lateness = {
      settling_delay = "string"
    }
    request_source = {
      dataframe_schema = "string"
      flat_schema = {
        fields = [{
          data_type = "string"
          name      = "string"
        }]
      }
    }
    sdp_source = {
      dataframe_schema   = "string"
      filter_condition   = "string"
      input_tables       = ["string"]
      output_table       = "string"
      pipeline_id        = "string"
      pipeline_state     = "string"
      transformation_sql = "string"
    }
    stream_source = {
      full_name          = "string"
      dataframe_schema   = "string"
      filter_condition   = "string"
      transformation_sql = "string"
    }
  }
  description = "string"
  entities {
    name = "string"
  }
  filter_condition = "string"
  inputs           = ["string"]
  lineage_context = {
    job_context = {
      job_id     = 0
      job_run_id = 0
    }
    notebook_id = 0
  }
  provider_config = {
    workspace_id = "string"
  }
  time_window = {
    continuous = {
      window_duration = "string"
      offset          = "string"
    }
    rolling = {
      delay           = "string"
      window_duration = "string"
    }
    sawtooth = {
      delay           = "string"
      window_duration = "string"
    }
    sliding = {
      slide_duration  = "string"
      delay           = "string"
      offset          = "string"
      window_duration = "string"
    }
    start_time = "string"
    tumbling = {
      window_duration = "string"
      delay           = "string"
      offset          = "string"
    }
  }
  timeseries_column = {
    name = "string"
  }
}

    var featureEngineeringFeatureResource = new FeatureEngineeringFeature("featureEngineeringFeatureResource", FeatureEngineeringFeatureArgs.builder()
    .fullName("string")
    .function(FeatureEngineeringFeatureFunctionArgs.builder()
        .aggregationFunction(FeatureEngineeringFeatureFunctionAggregationFunctionArgs.builder()
            .approxCountDistinct(FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinctArgs.builder()
                .input("string")
                .relativeSd(0.0)
                .build())
            .approxPercentile(FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentileArgs.builder()
                .input("string")
                .percentile(0.0)
                .accuracy(0)
                .build())
            .avg(FeatureEngineeringFeatureFunctionAggregationFunctionAvgArgs.builder()
                .input("string")
                .build())
            .countFunction(FeatureEngineeringFeatureFunctionAggregationFunctionCountFunctionArgs.builder()
                .input("string")
                .build())
            .first(FeatureEngineeringFeatureFunctionAggregationFunctionFirstArgs.builder()
                .input("string")
                .build())
            .firstDistinct(FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinctArgs.builder()
                .input("string")
                .n(0)
                .build())
            .firstN(FeatureEngineeringFeatureFunctionAggregationFunctionFirstNArgs.builder()
                .input("string")
                .n(0)
                .build())
            .last(FeatureEngineeringFeatureFunctionAggregationFunctionLastArgs.builder()
                .input("string")
                .build())
            .lastDistinct(FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinctArgs.builder()
                .input("string")
                .n(0)
                .build())
            .lastN(FeatureEngineeringFeatureFunctionAggregationFunctionLastNArgs.builder()
                .input("string")
                .n(0)
                .build())
            .max(FeatureEngineeringFeatureFunctionAggregationFunctionMaxArgs.builder()
                .input("string")
                .build())
            .min(FeatureEngineeringFeatureFunctionAggregationFunctionMinArgs.builder()
                .input("string")
                .build())
            .stddevPop(FeatureEngineeringFeatureFunctionAggregationFunctionStddevPopArgs.builder()
                .input("string")
                .build())
            .stddevSamp(FeatureEngineeringFeatureFunctionAggregationFunctionStddevSampArgs.builder()
                .input("string")
                .build())
            .sum(FeatureEngineeringFeatureFunctionAggregationFunctionSumArgs.builder()
                .input("string")
                .build())
            .timeWindow(FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowArgs.builder()
                .continuous(FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuousArgs.builder()
                    .windowDuration("string")
                    .offset("string")
                    .build())
                .rolling(FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRollingArgs.builder()
                    .delay("string")
                    .windowDuration("string")
                    .build())
                .sawtooth(FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtoothArgs.builder()
                    .delay("string")
                    .windowDuration("string")
                    .build())
                .sliding(FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSlidingArgs.builder()
                    .slideDuration("string")
                    .delay("string")
                    .offset("string")
                    .windowDuration("string")
                    .build())
                .startTime("string")
                .tumbling(FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumblingArgs.builder()
                    .windowDuration("string")
                    .delay("string")
                    .offset("string")
                    .build())
                .build())
            .varPop(FeatureEngineeringFeatureFunctionAggregationFunctionVarPopArgs.builder()
                .input("string")
                .build())
            .varSamp(FeatureEngineeringFeatureFunctionAggregationFunctionVarSampArgs.builder()
                .input("string")
                .build())
            .build())
        .columnSelection(FeatureEngineeringFeatureFunctionColumnSelectionArgs.builder()
            .column("string")
            .build())
        .customUdf(FeatureEngineeringFeatureFunctionCustomUdfArgs.builder()
            .functionPath("string")
            .inputBindings(FeatureEngineeringFeatureFunctionCustomUdfInputBindingArgs.builder()
                .column("string")
                .parameter("string")
                .build())
            .build())
        .extraParameters(FeatureEngineeringFeatureFunctionExtraParameterArgs.builder()
            .key("string")
            .value("string")
            .build())
        .functionType("string")
        .build())
    .source(FeatureEngineeringFeatureSourceArgs.builder()
        .deltaTableSource(FeatureEngineeringFeatureSourceDeltaTableSourceArgs.builder()
            .fullName("string")
            .dataframeSchema("string")
            .entityColumns("string")
            .filterCondition("string")
            .timeseriesColumn("string")
            .transformationSql("string")
            .build())
        .featureViewSource(FeatureEngineeringFeatureSourceFeatureViewSourceArgs.builder()
            .featureReferences(FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReferenceArgs.builder()
                .feature("string")
                .build())
            .build())
        .kafkaSource(FeatureEngineeringFeatureSourceKafkaSourceArgs.builder()
            .name("string")
            .entityColumnIdentifiers(FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArgs.builder()
                .variantExprPath("string")
                .build())
            .filterCondition("string")
            .timeseriesColumnIdentifier(FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifierArgs.builder()
                .variantExprPath("string")
                .build())
            .build())
        .lateness(FeatureEngineeringFeatureSourceLatenessArgs.builder()
            .settlingDelay("string")
            .build())
        .requestSource(FeatureEngineeringFeatureSourceRequestSourceArgs.builder()
            .dataframeSchema("string")
            .flatSchema(FeatureEngineeringFeatureSourceRequestSourceFlatSchemaArgs.builder()
                .fields(FeatureEngineeringFeatureSourceRequestSourceFlatSchemaFieldArgs.builder()
                    .dataType("string")
                    .name("string")
                    .build())
                .build())
            .build())
        .sdpSource(FeatureEngineeringFeatureSourceSdpSourceArgs.builder()
            .dataframeSchema("string")
            .filterCondition("string")
            .inputTables("string")
            .outputTable("string")
            .pipelineId("string")
            .pipelineState("string")
            .transformationSql("string")
            .build())
        .streamSource(FeatureEngineeringFeatureSourceStreamSourceArgs.builder()
            .fullName("string")
            .dataframeSchema("string")
            .filterCondition("string")
            .transformationSql("string")
            .build())
        .build())
    .description("string")
    .entities(FeatureEngineeringFeatureEntityArgs.builder()
        .name("string")
        .build())
    .filterCondition("string")
    .inputs("string")
    .lineageContext(FeatureEngineeringFeatureLineageContextArgs.builder()
        .jobContext(FeatureEngineeringFeatureLineageContextJobContextArgs.builder()
            .jobId(0)
            .jobRunId(0)
            .build())
        .notebookId(0)
        .build())
    .providerConfig(FeatureEngineeringFeatureProviderConfigArgs.builder()
        .workspaceId("string")
        .build())
    .timeWindow(FeatureEngineeringFeatureTimeWindowArgs.builder()
        .continuous(FeatureEngineeringFeatureTimeWindowContinuousArgs.builder()
            .windowDuration("string")
            .offset("string")
            .build())
        .rolling(FeatureEngineeringFeatureTimeWindowRollingArgs.builder()
            .delay("string")
            .windowDuration("string")
            .build())
        .sawtooth(FeatureEngineeringFeatureTimeWindowSawtoothArgs.builder()
            .delay("string")
            .windowDuration("string")
            .build())
        .sliding(FeatureEngineeringFeatureTimeWindowSlidingArgs.builder()
            .slideDuration("string")
            .delay("string")
            .offset("string")
            .windowDuration("string")
            .build())
        .startTime("string")
        .tumbling(FeatureEngineeringFeatureTimeWindowTumblingArgs.builder()
            .windowDuration("string")
            .delay("string")
            .offset("string")
            .build())
        .build())
    .timeseriesColumn(FeatureEngineeringFeatureTimeseriesColumnArgs.builder()
        .name("string")
        .build())
    .build());

    feature_engineering_feature_resource = databricks.FeatureEngineeringFeature("featureEngineeringFeatureResource",
    full_name="string",
    function={
        "aggregation_function": {
            "approx_count_distinct": {
                "input": "string",
                "relative_sd": float(0),
            },
            "approx_percentile": {
                "input": "string",
                "percentile": float(0),
                "accuracy": 0,
            },
            "avg": {
                "input": "string",
            },
            "count_function": {
                "input": "string",
            },
            "first": {
                "input": "string",
            },
            "first_distinct": {
                "input": "string",
                "n": 0,
            },
            "first_n": {
                "input": "string",
                "n": 0,
            },
            "last": {
                "input": "string",
            },
            "last_distinct": {
                "input": "string",
                "n": 0,
            },
            "last_n": {
                "input": "string",
                "n": 0,
            },
            "max": {
                "input": "string",
            },
            "min": {
                "input": "string",
            },
            "stddev_pop": {
                "input": "string",
            },
            "stddev_samp": {
                "input": "string",
            },
            "sum": {
                "input": "string",
            },
            "time_window": {
                "continuous": {
                    "window_duration": "string",
                    "offset": "string",
                },
                "rolling": {
                    "delay": "string",
                    "window_duration": "string",
                },
                "sawtooth": {
                    "delay": "string",
                    "window_duration": "string",
                },
                "sliding": {
                    "slide_duration": "string",
                    "delay": "string",
                    "offset": "string",
                    "window_duration": "string",
                },
                "start_time": "string",
                "tumbling": {
                    "window_duration": "string",
                    "delay": "string",
                    "offset": "string",
                },
            },
            "var_pop": {
                "input": "string",
            },
            "var_samp": {
                "input": "string",
            },
        },
        "column_selection": {
            "column": "string",
        },
        "custom_udf": {
            "function_path": "string",
            "input_bindings": [{
                "column": "string",
                "parameter": "string",
            }],
        },
        "extra_parameters": [{
            "key": "string",
            "value": "string",
        }],
        "function_type": "string",
    },
    source={
        "delta_table_source": {
            "full_name": "string",
            "dataframe_schema": "string",
            "entity_columns": ["string"],
            "filter_condition": "string",
            "timeseries_column": "string",
            "transformation_sql": "string",
        },
        "feature_view_source": {
            "feature_references": [{
                "feature": "string",
            }],
        },
        "kafka_source": {
            "name": "string",
            "entity_column_identifiers": [{
                "variant_expr_path": "string",
            }],
            "filter_condition": "string",
            "timeseries_column_identifier": {
                "variant_expr_path": "string",
            },
        },
        "lateness": {
            "settling_delay": "string",
        },
        "request_source": {
            "dataframe_schema": "string",
            "flat_schema": {
                "fields": [{
                    "data_type": "string",
                    "name": "string",
                }],
            },
        },
        "sdp_source": {
            "dataframe_schema": "string",
            "filter_condition": "string",
            "input_tables": ["string"],
            "output_table": "string",
            "pipeline_id": "string",
            "pipeline_state": "string",
            "transformation_sql": "string",
        },
        "stream_source": {
            "full_name": "string",
            "dataframe_schema": "string",
            "filter_condition": "string",
            "transformation_sql": "string",
        },
    },
    description="string",
    entities=[{
        "name": "string",
    }],
    filter_condition="string",
    inputs=["string"],
    lineage_context={
        "job_context": {
            "job_id": 0,
            "job_run_id": 0,
        },
        "notebook_id": 0,
    },
    provider_config={
        "workspace_id": "string",
    },
    time_window={
        "continuous": {
            "window_duration": "string",
            "offset": "string",
        },
        "rolling": {
            "delay": "string",
            "window_duration": "string",
        },
        "sawtooth": {
            "delay": "string",
            "window_duration": "string",
        },
        "sliding": {
            "slide_duration": "string",
            "delay": "string",
            "offset": "string",
            "window_duration": "string",
        },
        "start_time": "string",
        "tumbling": {
            "window_duration": "string",
            "delay": "string",
            "offset": "string",
        },
    },
    timeseries_column={
        "name": "string",
    })

    const featureEngineeringFeatureResource = new databricks.FeatureEngineeringFeature("featureEngineeringFeatureResource", {
    fullName: "string",
    "function": {
        aggregationFunction: {
            approxCountDistinct: {
                input: "string",
                relativeSd: 0,
            },
            approxPercentile: {
                input: "string",
                percentile: 0,
                accuracy: 0,
            },
            avg: {
                input: "string",
            },
            countFunction: {
                input: "string",
            },
            first: {
                input: "string",
            },
            firstDistinct: {
                input: "string",
                n: 0,
            },
            firstN: {
                input: "string",
                n: 0,
            },
            last: {
                input: "string",
            },
            lastDistinct: {
                input: "string",
                n: 0,
            },
            lastN: {
                input: "string",
                n: 0,
            },
            max: {
                input: "string",
            },
            min: {
                input: "string",
            },
            stddevPop: {
                input: "string",
            },
            stddevSamp: {
                input: "string",
            },
            sum: {
                input: "string",
            },
            timeWindow: {
                continuous: {
                    windowDuration: "string",
                    offset: "string",
                },
                rolling: {
                    delay: "string",
                    windowDuration: "string",
                },
                sawtooth: {
                    delay: "string",
                    windowDuration: "string",
                },
                sliding: {
                    slideDuration: "string",
                    delay: "string",
                    offset: "string",
                    windowDuration: "string",
                },
                startTime: "string",
                tumbling: {
                    windowDuration: "string",
                    delay: "string",
                    offset: "string",
                },
            },
            varPop: {
                input: "string",
            },
            varSamp: {
                input: "string",
            },
        },
        columnSelection: {
            column: "string",
        },
        customUdf: {
            functionPath: "string",
            inputBindings: [{
                column: "string",
                parameter: "string",
            }],
        },
        extraParameters: [{
            key: "string",
            value: "string",
        }],
        functionType: "string",
    },
    source: {
        deltaTableSource: {
            fullName: "string",
            dataframeSchema: "string",
            entityColumns: ["string"],
            filterCondition: "string",
            timeseriesColumn: "string",
            transformationSql: "string",
        },
        featureViewSource: {
            featureReferences: [{
                feature: "string",
            }],
        },
        kafkaSource: {
            name: "string",
            entityColumnIdentifiers: [{
                variantExprPath: "string",
            }],
            filterCondition: "string",
            timeseriesColumnIdentifier: {
                variantExprPath: "string",
            },
        },
        lateness: {
            settlingDelay: "string",
        },
        requestSource: {
            dataframeSchema: "string",
            flatSchema: {
                fields: [{
                    dataType: "string",
                    name: "string",
                }],
            },
        },
        sdpSource: {
            dataframeSchema: "string",
            filterCondition: "string",
            inputTables: ["string"],
            outputTable: "string",
            pipelineId: "string",
            pipelineState: "string",
            transformationSql: "string",
        },
        streamSource: {
            fullName: "string",
            dataframeSchema: "string",
            filterCondition: "string",
            transformationSql: "string",
        },
    },
    description: "string",
    entities: [{
        name: "string",
    }],
    filterCondition: "string",
    inputs: ["string"],
    lineageContext: {
        jobContext: {
            jobId: 0,
            jobRunId: 0,
        },
        notebookId: 0,
    },
    providerConfig: {
        workspaceId: "string",
    },
    timeWindow: {
        continuous: {
            windowDuration: "string",
            offset: "string",
        },
        rolling: {
            delay: "string",
            windowDuration: "string",
        },
        sawtooth: {
            delay: "string",
            windowDuration: "string",
        },
        sliding: {
            slideDuration: "string",
            delay: "string",
            offset: "string",
            windowDuration: "string",
        },
        startTime: "string",
        tumbling: {
            windowDuration: "string",
            delay: "string",
            offset: "string",
        },
    },
    timeseriesColumn: {
        name: "string",
    },
});

    type: databricks:FeatureEngineeringFeature
properties:
    description: string
    entities:
        - name: string
    filterCondition: string
    fullName: string
    function:
        aggregationFunction:
            approxCountDistinct:
                input: string
                relativeSd: 0
            approxPercentile:
                accuracy: 0
                input: string
                percentile: 0
            avg:
                input: string
            countFunction:
                input: string
            first:
                input: string
            firstDistinct:
                input: string
                "n": 0
            firstN:
                input: string
                "n": 0
            last:
                input: string
            lastDistinct:
                input: string
                "n": 0
            lastN:
                input: string
                "n": 0
            max:
                input: string
            min:
                input: string
            stddevPop:
                input: string
            stddevSamp:
                input: string
            sum:
                input: string
            timeWindow:
                continuous:
                    offset: string
                    windowDuration: string
                rolling:
                    delay: string
                    windowDuration: string
                sawtooth:
                    delay: string
                    windowDuration: string
                sliding:
                    delay: string
                    offset: string
                    slideDuration: string
                    windowDuration: string
                startTime: string
                tumbling:
                    delay: string
                    offset: string
                    windowDuration: string
            varPop:
                input: string
            varSamp:
                input: string
        columnSelection:
            column: string
        customUdf:
            functionPath: string
            inputBindings:
                - column: string
                  parameter: string
        extraParameters:
            - key: string
              value: string
        functionType: string
    inputs:
        - string
    lineageContext:
        jobContext:
            jobId: 0
            jobRunId: 0
        notebookId: 0
    providerConfig:
        workspaceId: string
    source:
        deltaTableSource:
            dataframeSchema: string
            entityColumns:
                - string
            filterCondition: string
            fullName: string
            timeseriesColumn: string
            transformationSql: string
        featureViewSource:
            featureReferences:
                - feature: string
        kafkaSource:
            entityColumnIdentifiers:
                - variantExprPath: string
            filterCondition: string
            name: string
            timeseriesColumnIdentifier:
                variantExprPath: string
        lateness:
            settlingDelay: string
        requestSource:
            dataframeSchema: string
            flatSchema:
                fields:
                    - dataType: string
                      name: string
        sdpSource:
            dataframeSchema: string
            filterCondition: string
            inputTables:
                - string
            outputTable: string
            pipelineId: string
            pipelineState: string
            transformationSql: string
        streamSource:
            dataframeSchema: string
            filterCondition: string
            fullName: string
            transformationSql: string
    timeWindow:
        continuous:
            offset: string
            windowDuration: string
        rolling:
            delay: string
            windowDuration: string
        sawtooth:
            delay: string
            windowDuration: string
        sliding:
            delay: string
            offset: string
            slideDuration: string
            windowDuration: string
        startTime: string
        tumbling:
            delay: string
            offset: string
            windowDuration: string
    timeseriesColumn:
        name: 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
    Source FeatureEngineeringFeatureSource
    The data source of the feature
    Description string
    The description of the feature
    Entities List<FeatureEngineeringFeatureEntity>
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    FilterCondition string
    Inputs List<string>
    LineageContext FeatureEngineeringFeatureLineageContext
    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
    ProviderConfig FeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    TimeWindow FeatureEngineeringFeatureTimeWindow
    TimeseriesColumn FeatureEngineeringFeatureTimeseriesColumn
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    FullName string
    The full three-part name (catalog, schema, name) of the feature
    Function FeatureEngineeringFeatureFunctionArgs
    The function by which the feature is computed
    Source FeatureEngineeringFeatureSourceArgs
    The data source of the feature
    Description string
    The description of the feature
    Entities []FeatureEngineeringFeatureEntityArgs
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    FilterCondition string
    Inputs []string
    LineageContext FeatureEngineeringFeatureLineageContextArgs
    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
    ProviderConfig FeatureEngineeringFeatureProviderConfigArgs
    Configure the provider for management through account provider.
    TimeWindow FeatureEngineeringFeatureTimeWindowArgs
    TimeseriesColumn FeatureEngineeringFeatureTimeseriesColumnArgs
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    full_name string
    The full three-part name (catalog, schema, name) of the feature
    function object
    The function by which the feature is computed
    source object
    The data source of the feature
    description string
    The description of the feature
    entities list(object)
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filter_condition string
    inputs list(string)
    lineage_context object
    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
    provider_config object
    Configure the provider for management through account provider.
    time_window object
    timeseries_column object
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunction
    The function by which the feature is computed
    source FeatureEngineeringFeatureSource
    The data source of the feature
    description String
    The description of the feature
    entities List<FeatureEngineeringFeatureEntity>
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filterCondition String
    inputs List<String>
    lineageContext FeatureEngineeringFeatureLineageContext
    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
    providerConfig FeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    timeWindow FeatureEngineeringFeatureTimeWindow
    timeseriesColumn FeatureEngineeringFeatureTimeseriesColumn
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    fullName string
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunction
    The function by which the feature is computed
    source FeatureEngineeringFeatureSource
    The data source of the feature
    description string
    The description of the feature
    entities FeatureEngineeringFeatureEntity[]
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filterCondition string
    inputs string[]
    lineageContext FeatureEngineeringFeatureLineageContext
    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
    providerConfig FeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    timeWindow FeatureEngineeringFeatureTimeWindow
    timeseriesColumn FeatureEngineeringFeatureTimeseriesColumn
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    full_name str
    The full three-part name (catalog, schema, name) of the feature
    function FeatureEngineeringFeatureFunctionArgs
    The function by which the feature is computed
    source FeatureEngineeringFeatureSourceArgs
    The data source of the feature
    description str
    The description of the feature
    entities Sequence[FeatureEngineeringFeatureEntityArgs]
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filter_condition str
    inputs Sequence[str]
    lineage_context FeatureEngineeringFeatureLineageContextArgs
    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
    provider_config FeatureEngineeringFeatureProviderConfigArgs
    Configure the provider for management through account provider.
    time_window FeatureEngineeringFeatureTimeWindowArgs
    timeseries_column FeatureEngineeringFeatureTimeseriesColumnArgs
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    function Property Map
    The function by which the feature is computed
    source Property Map
    The data source of the feature
    description String
    The description of the feature
    entities List<Property Map>
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filterCondition String
    inputs List<String>
    lineageContext Property Map
    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
    providerConfig Property Map
    Configure the provider for management through account provider.
    timeWindow Property Map
    timeseriesColumn Property Map
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features

    Outputs

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

    CatalogName string
    (string) - Name of parent catalog
    CreatedAt string
    (string) - Time at which this feature was created
    CreatedBy string
    (string) - Username of the feature creator
    Id string
    The provider-assigned unique ID for this managed resource.
    Name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    SchemaName string
    (string) - Name of parent schema relative to its parent catalog
    CatalogName string
    (string) - Name of parent catalog
    CreatedAt string
    (string) - Time at which this feature was created
    CreatedBy string
    (string) - Username of the feature creator
    Id string
    The provider-assigned unique ID for this managed resource.
    Name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    SchemaName string
    (string) - Name of parent schema relative to its parent catalog
    catalog_name string
    (string) - Name of parent catalog
    created_at string
    (string) - Time at which this feature was created
    created_by string
    (string) - Username of the feature creator
    id string
    The provider-assigned unique ID for this managed resource.
    name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    schema_name string
    (string) - Name of parent schema relative to its parent catalog
    catalogName String
    (string) - Name of parent catalog
    createdAt String
    (string) - Time at which this feature was created
    createdBy String
    (string) - Username of the feature creator
    id String
    The provider-assigned unique ID for this managed resource.
    name String
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    schemaName String
    (string) - Name of parent schema relative to its parent catalog
    catalogName string
    (string) - Name of parent catalog
    createdAt string
    (string) - Time at which this feature was created
    createdBy string
    (string) - Username of the feature creator
    id string
    The provider-assigned unique ID for this managed resource.
    name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    schemaName string
    (string) - Name of parent schema relative to its parent catalog
    catalog_name str
    (string) - Name of parent catalog
    created_at str
    (string) - Time at which this feature was created
    created_by str
    (string) - Username of the feature creator
    id str
    The provider-assigned unique ID for this managed resource.
    name str
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    schema_name str
    (string) - Name of parent schema relative to its parent catalog
    catalogName String
    (string) - Name of parent catalog
    createdAt String
    (string) - Time at which this feature was created
    createdBy String
    (string) - Username of the feature creator
    id String
    The provider-assigned unique ID for this managed resource.
    name String
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    schemaName String
    (string) - Name of parent schema relative to its parent catalog

    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,
        catalog_name: Optional[str] = None,
        created_at: Optional[str] = None,
        created_by: Optional[str] = None,
        description: Optional[str] = None,
        entities: Optional[Sequence[FeatureEngineeringFeatureEntityArgs]] = 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,
        name: Optional[str] = None,
        provider_config: Optional[FeatureEngineeringFeatureProviderConfigArgs] = None,
        schema_name: Optional[str] = None,
        source: Optional[FeatureEngineeringFeatureSourceArgs] = None,
        time_window: Optional[FeatureEngineeringFeatureTimeWindowArgs] = None,
        timeseries_column: Optional[FeatureEngineeringFeatureTimeseriesColumnArgs] = 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: ${id}
    import {
  to = databricks_feature_engineering_feature.example
  id = "${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:
    CatalogName string
    (string) - Name of parent catalog
    CreatedAt string
    (string) - Time at which this feature was created
    CreatedBy string
    (string) - Username of the feature creator
    Description string
    The description of the feature
    Entities List<FeatureEngineeringFeatureEntity>
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    FilterCondition string
    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>
    LineageContext FeatureEngineeringFeatureLineageContext
    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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    ProviderConfig FeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    SchemaName string
    (string) - Name of parent schema relative to its parent catalog
    Source FeatureEngineeringFeatureSource
    The data source of the feature
    TimeWindow FeatureEngineeringFeatureTimeWindow
    TimeseriesColumn FeatureEngineeringFeatureTimeseriesColumn
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    CatalogName string
    (string) - Name of parent catalog
    CreatedAt string
    (string) - Time at which this feature was created
    CreatedBy string
    (string) - Username of the feature creator
    Description string
    The description of the feature
    Entities []FeatureEngineeringFeatureEntityArgs
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    FilterCondition string
    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
    LineageContext FeatureEngineeringFeatureLineageContextArgs
    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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    ProviderConfig FeatureEngineeringFeatureProviderConfigArgs
    Configure the provider for management through account provider.
    SchemaName string
    (string) - Name of parent schema relative to its parent catalog
    Source FeatureEngineeringFeatureSourceArgs
    The data source of the feature
    TimeWindow FeatureEngineeringFeatureTimeWindowArgs
    TimeseriesColumn FeatureEngineeringFeatureTimeseriesColumnArgs
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    catalog_name string
    (string) - Name of parent catalog
    created_at string
    (string) - Time at which this feature was created
    created_by string
    (string) - Username of the feature creator
    description string
    The description of the feature
    entities list(object)
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filter_condition string
    full_name string
    The full three-part name (catalog, schema, name) of the feature
    function object
    The function by which the feature is computed
    inputs list(string)
    lineage_context object
    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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    provider_config object
    Configure the provider for management through account provider.
    schema_name string
    (string) - Name of parent schema relative to its parent catalog
    source object
    The data source of the feature
    time_window object
    timeseries_column object
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    catalogName String
    (string) - Name of parent catalog
    createdAt String
    (string) - Time at which this feature was created
    createdBy String
    (string) - Username of the feature creator
    description String
    The description of the feature
    entities List<FeatureEngineeringFeatureEntity>
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filterCondition String
    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>
    lineageContext FeatureEngineeringFeatureLineageContext
    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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    providerConfig FeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    schemaName String
    (string) - Name of parent schema relative to its parent catalog
    source FeatureEngineeringFeatureSource
    The data source of the feature
    timeWindow FeatureEngineeringFeatureTimeWindow
    timeseriesColumn FeatureEngineeringFeatureTimeseriesColumn
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    catalogName string
    (string) - Name of parent catalog
    createdAt string
    (string) - Time at which this feature was created
    createdBy string
    (string) - Username of the feature creator
    description string
    The description of the feature
    entities FeatureEngineeringFeatureEntity[]
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filterCondition string
    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[]
    lineageContext FeatureEngineeringFeatureLineageContext
    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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    providerConfig FeatureEngineeringFeatureProviderConfig
    Configure the provider for management through account provider.
    schemaName string
    (string) - Name of parent schema relative to its parent catalog
    source FeatureEngineeringFeatureSource
    The data source of the feature
    timeWindow FeatureEngineeringFeatureTimeWindow
    timeseriesColumn FeatureEngineeringFeatureTimeseriesColumn
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    catalog_name str
    (string) - Name of parent catalog
    created_at str
    (string) - Time at which this feature was created
    created_by str
    (string) - Username of the feature creator
    description str
    The description of the feature
    entities Sequence[FeatureEngineeringFeatureEntityArgs]
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filter_condition str
    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]
    lineage_context FeatureEngineeringFeatureLineageContextArgs
    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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    provider_config FeatureEngineeringFeatureProviderConfigArgs
    Configure the provider for management through account provider.
    schema_name str
    (string) - Name of parent schema relative to its parent catalog
    source FeatureEngineeringFeatureSourceArgs
    The data source of the feature
    time_window FeatureEngineeringFeatureTimeWindowArgs
    timeseries_column FeatureEngineeringFeatureTimeseriesColumnArgs
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    catalogName String
    (string) - Name of parent catalog
    createdAt String
    (string) - Time at which this feature was created
    createdBy String
    (string) - Username of the feature creator
    description String
    The description of the feature
    entities List<Property Map>
    The entity columns for the feature, used as aggregation keys and for query-time lookup. Optional since entities are not set for RequestSource features or on-demand calculated features
    filterCondition String
    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>
    lineageContext Property Map
    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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    providerConfig Property Map
    Configure the provider for management through account provider.
    schemaName String
    (string) - Name of parent schema relative to its parent catalog
    source Property Map
    The data source of the feature
    timeWindow Property Map
    timeseriesColumn Property Map
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features

    Supporting Types

    FeatureEngineeringFeatureEntity, FeatureEngineeringFeatureEntityArgs

    Name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    Name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name String
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name str
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name String
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)

    FeatureEngineeringFeatureFunction, FeatureEngineeringFeatureFunctionArgs

    AggregationFunction FeatureEngineeringFeatureFunctionAggregationFunction
    An aggregation function applied over a time window
    ColumnSelection FeatureEngineeringFeatureFunctionColumnSelection
    Selects the latest value of a single column in a data source
    CustomUdf FeatureEngineeringFeatureFunctionCustomUdf
    Applies a registered Unity Catalog function row-wise to source columns
    ExtraParameters List<FeatureEngineeringFeatureFunctionExtraParameter>
    FunctionType string
    Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, FUNCTION_TYPE_UNSPECIFIED, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    AggregationFunction FeatureEngineeringFeatureFunctionAggregationFunction
    An aggregation function applied over a time window
    ColumnSelection FeatureEngineeringFeatureFunctionColumnSelection
    Selects the latest value of a single column in a data source
    CustomUdf FeatureEngineeringFeatureFunctionCustomUdf
    Applies a registered Unity Catalog function row-wise to source columns
    ExtraParameters []FeatureEngineeringFeatureFunctionExtraParameter
    FunctionType string
    Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, FUNCTION_TYPE_UNSPECIFIED, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    aggregation_function object
    An aggregation function applied over a time window
    column_selection object
    Selects the latest value of a single column in a data source
    custom_udf object
    Applies a registered Unity Catalog function row-wise to source columns
    extra_parameters list(object)
    function_type string
    Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, FUNCTION_TYPE_UNSPECIFIED, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    aggregationFunction FeatureEngineeringFeatureFunctionAggregationFunction
    An aggregation function applied over a time window
    columnSelection FeatureEngineeringFeatureFunctionColumnSelection
    Selects the latest value of a single column in a data source
    customUdf FeatureEngineeringFeatureFunctionCustomUdf
    Applies a registered Unity Catalog function row-wise to source columns
    extraParameters List<FeatureEngineeringFeatureFunctionExtraParameter>
    functionType String
    Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, FUNCTION_TYPE_UNSPECIFIED, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    aggregationFunction FeatureEngineeringFeatureFunctionAggregationFunction
    An aggregation function applied over a time window
    columnSelection FeatureEngineeringFeatureFunctionColumnSelection
    Selects the latest value of a single column in a data source
    customUdf FeatureEngineeringFeatureFunctionCustomUdf
    Applies a registered Unity Catalog function row-wise to source columns
    extraParameters FeatureEngineeringFeatureFunctionExtraParameter[]
    functionType string
    Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, FUNCTION_TYPE_UNSPECIFIED, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    aggregation_function FeatureEngineeringFeatureFunctionAggregationFunction
    An aggregation function applied over a time window
    column_selection FeatureEngineeringFeatureFunctionColumnSelection
    Selects the latest value of a single column in a data source
    custom_udf FeatureEngineeringFeatureFunctionCustomUdf
    Applies a registered Unity Catalog function row-wise to source columns
    extra_parameters Sequence[FeatureEngineeringFeatureFunctionExtraParameter]
    function_type str
    Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, FUNCTION_TYPE_UNSPECIFIED, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP
    aggregationFunction Property Map
    An aggregation function applied over a time window
    columnSelection Property Map
    Selects the latest value of a single column in a data source
    customUdf Property Map
    Applies a registered Unity Catalog function row-wise to source columns
    extraParameters List<Property Map>
    functionType String
    Possible values are: APPROX_COUNT_DISTINCT, APPROX_PERCENTILE, AVG, COUNT, FIRST, FUNCTION_TYPE_UNSPECIFIED, LAST, MAX, MIN, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP

    FeatureEngineeringFeatureFunctionAggregationFunction, FeatureEngineeringFeatureFunctionAggregationFunctionArgs

    ApproxCountDistinct FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    ApproxPercentile FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    Avg FeatureEngineeringFeatureFunctionAggregationFunctionAvg
    CountFunction FeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    First FeatureEngineeringFeatureFunctionAggregationFunctionFirst
    FirstDistinct FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    FirstN FeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    Last FeatureEngineeringFeatureFunctionAggregationFunctionLast
    LastDistinct FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    LastN FeatureEngineeringFeatureFunctionAggregationFunctionLastN
    Max FeatureEngineeringFeatureFunctionAggregationFunctionMax
    Min FeatureEngineeringFeatureFunctionAggregationFunctionMin
    StddevPop FeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    StddevSamp FeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    Sum FeatureEngineeringFeatureFunctionAggregationFunctionSum
    TimeWindow FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    VarPop FeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    VarSamp FeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    ApproxCountDistinct FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    ApproxPercentile FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    Avg FeatureEngineeringFeatureFunctionAggregationFunctionAvg
    CountFunction FeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    First FeatureEngineeringFeatureFunctionAggregationFunctionFirst
    FirstDistinct FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    FirstN FeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    Last FeatureEngineeringFeatureFunctionAggregationFunctionLast
    LastDistinct FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    LastN FeatureEngineeringFeatureFunctionAggregationFunctionLastN
    Max FeatureEngineeringFeatureFunctionAggregationFunctionMax
    Min FeatureEngineeringFeatureFunctionAggregationFunctionMin
    StddevPop FeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    StddevSamp FeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    Sum FeatureEngineeringFeatureFunctionAggregationFunctionSum
    TimeWindow FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    VarPop FeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    VarSamp FeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    approxCountDistinct FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    approxPercentile FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    avg FeatureEngineeringFeatureFunctionAggregationFunctionAvg
    countFunction FeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    first FeatureEngineeringFeatureFunctionAggregationFunctionFirst
    firstDistinct FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    firstN FeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    last FeatureEngineeringFeatureFunctionAggregationFunctionLast
    lastDistinct FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    lastN FeatureEngineeringFeatureFunctionAggregationFunctionLastN
    max FeatureEngineeringFeatureFunctionAggregationFunctionMax
    min FeatureEngineeringFeatureFunctionAggregationFunctionMin
    stddevPop FeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    stddevSamp FeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    sum FeatureEngineeringFeatureFunctionAggregationFunctionSum
    timeWindow FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    varPop FeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    varSamp FeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    approxCountDistinct FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    approxPercentile FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    avg FeatureEngineeringFeatureFunctionAggregationFunctionAvg
    countFunction FeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    first FeatureEngineeringFeatureFunctionAggregationFunctionFirst
    firstDistinct FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    firstN FeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    last FeatureEngineeringFeatureFunctionAggregationFunctionLast
    lastDistinct FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    lastN FeatureEngineeringFeatureFunctionAggregationFunctionLastN
    max FeatureEngineeringFeatureFunctionAggregationFunctionMax
    min FeatureEngineeringFeatureFunctionAggregationFunctionMin
    stddevPop FeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    stddevSamp FeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    sum FeatureEngineeringFeatureFunctionAggregationFunctionSum
    timeWindow FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    varPop FeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    varSamp FeatureEngineeringFeatureFunctionAggregationFunctionVarSamp
    approx_count_distinct FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct
    approx_percentile FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile
    avg FeatureEngineeringFeatureFunctionAggregationFunctionAvg
    count_function FeatureEngineeringFeatureFunctionAggregationFunctionCountFunction
    first FeatureEngineeringFeatureFunctionAggregationFunctionFirst
    first_distinct FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct
    first_n FeatureEngineeringFeatureFunctionAggregationFunctionFirstN
    last FeatureEngineeringFeatureFunctionAggregationFunctionLast
    last_distinct FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct
    last_n FeatureEngineeringFeatureFunctionAggregationFunctionLastN
    max FeatureEngineeringFeatureFunctionAggregationFunctionMax
    min FeatureEngineeringFeatureFunctionAggregationFunctionMin
    stddev_pop FeatureEngineeringFeatureFunctionAggregationFunctionStddevPop
    stddev_samp FeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp
    sum FeatureEngineeringFeatureFunctionAggregationFunctionSum
    time_window FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow
    var_pop FeatureEngineeringFeatureFunctionAggregationFunctionVarPop
    var_samp FeatureEngineeringFeatureFunctionAggregationFunctionVarSamp

    FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinct, FeatureEngineeringFeatureFunctionAggregationFunctionApproxCountDistinctArgs

    Input string
    RelativeSd double
    The maximum relative standard deviation allowed (default defined by Spark)
    Input string
    RelativeSd float64
    The maximum relative standard deviation allowed (default defined by Spark)
    input string
    relative_sd number
    The maximum relative standard deviation allowed (default defined by Spark)
    input String
    relativeSd Double
    The maximum relative standard deviation allowed (default defined by Spark)
    input string
    relativeSd number
    The maximum relative standard deviation allowed (default defined by Spark)
    input str
    relative_sd float
    The maximum relative standard deviation allowed (default defined by Spark)
    input String
    relativeSd Number
    The maximum relative standard deviation allowed (default defined by Spark)

    FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentile, FeatureEngineeringFeatureFunctionAggregationFunctionApproxPercentileArgs

    Input string
    Percentile double
    The percentile value to compute (between 0 and 1)
    Accuracy int
    The accuracy parameter (higher is more accurate but slower)
    Input string
    Percentile float64
    The percentile value to compute (between 0 and 1)
    Accuracy int
    The accuracy parameter (higher is more accurate but slower)
    input string
    percentile number
    The percentile value to compute (between 0 and 1)
    accuracy number
    The accuracy parameter (higher is more accurate but slower)
    input String
    percentile Double
    The percentile value to compute (between 0 and 1)
    accuracy Integer
    The accuracy parameter (higher is more accurate but slower)
    input string
    percentile number
    The percentile value to compute (between 0 and 1)
    accuracy number
    The accuracy parameter (higher is more accurate but slower)
    input str
    percentile float
    The percentile value to compute (between 0 and 1)
    accuracy int
    The accuracy parameter (higher is more accurate but slower)
    input String
    percentile Number
    The percentile value to compute (between 0 and 1)
    accuracy Number
    The accuracy parameter (higher is more accurate but slower)

    FeatureEngineeringFeatureFunctionAggregationFunctionAvg, FeatureEngineeringFeatureFunctionAggregationFunctionAvgArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionCountFunction, FeatureEngineeringFeatureFunctionAggregationFunctionCountFunctionArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionFirst, FeatureEngineeringFeatureFunctionAggregationFunctionFirstArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinct, FeatureEngineeringFeatureFunctionAggregationFunctionFirstDistinctArgs

    Input string
    N int
    Input string
    N int
    input string
    n number
    input String
    n Integer
    input string
    n number
    input str
    n int
    input String
    n Number

    FeatureEngineeringFeatureFunctionAggregationFunctionFirstN, FeatureEngineeringFeatureFunctionAggregationFunctionFirstNArgs

    Input string
    N int
    Input string
    N int
    input string
    n number
    input String
    n Integer
    input string
    n number
    input str
    n int
    input String
    n Number

    FeatureEngineeringFeatureFunctionAggregationFunctionLast, FeatureEngineeringFeatureFunctionAggregationFunctionLastArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinct, FeatureEngineeringFeatureFunctionAggregationFunctionLastDistinctArgs

    Input string
    N int
    Input string
    N int
    input string
    n number
    input String
    n Integer
    input string
    n number
    input str
    n int
    input String
    n Number

    FeatureEngineeringFeatureFunctionAggregationFunctionLastN, FeatureEngineeringFeatureFunctionAggregationFunctionLastNArgs

    Input string
    N int
    Input string
    N int
    input string
    n number
    input String
    n Integer
    input string
    n number
    input str
    n int
    input String
    n Number

    FeatureEngineeringFeatureFunctionAggregationFunctionMax, FeatureEngineeringFeatureFunctionAggregationFunctionMaxArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionMin, FeatureEngineeringFeatureFunctionAggregationFunctionMinArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionStddevPop, FeatureEngineeringFeatureFunctionAggregationFunctionStddevPopArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionStddevSamp, FeatureEngineeringFeatureFunctionAggregationFunctionStddevSampArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionSum, FeatureEngineeringFeatureFunctionAggregationFunctionSumArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindow, FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowArgs

    Continuous FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuous
    Rolling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    Sawtooth FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    Sliding FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    StartTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    Tumbling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    Continuous FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuous
    Rolling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    Sawtooth FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    Sliding FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    StartTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    Tumbling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    continuous object
    rolling object
    sawtooth object
    A sawtooth window served via the hybrid batch + streaming path
    sliding object
    start_time 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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling object
    continuous FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuous
    rolling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    sawtooth FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    sliding FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    startTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    continuous FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuous
    rolling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    sawtooth FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    sliding FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    startTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    continuous FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuous
    rolling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling
    sawtooth FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    sliding FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding
    start_time str
    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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling
    continuous Property Map
    rolling Property Map
    sawtooth Property Map
    A sawtooth window served via the hybrid batch + streaming path
    sliding Property Map
    startTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling Property Map

    FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuous, FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowContinuousArgs

    WindowDuration string
    Offset string
    WindowDuration string
    Offset string
    window_duration string
    offset string
    windowDuration String
    offset String
    windowDuration string
    offset string
    windowDuration String
    offset String

    FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRolling, FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowRollingArgs

    Delay string
    WindowDuration string
    Delay string
    WindowDuration string
    delay string
    window_duration string
    delay String
    windowDuration String
    delay string
    windowDuration string
    delay String
    windowDuration String

    FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtooth, FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSawtoothArgs

    Delay string
    WindowDuration string
    Delay string
    WindowDuration string
    delay string
    window_duration string
    delay String
    windowDuration String
    delay string
    windowDuration string
    delay String
    windowDuration String

    FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSliding, FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowSlidingArgs

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

    FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumbling, FeatureEngineeringFeatureFunctionAggregationFunctionTimeWindowTumblingArgs

    WindowDuration string
    Delay string
    Offset string
    WindowDuration string
    Delay string
    Offset string
    window_duration string
    delay string
    offset string
    windowDuration String
    delay String
    offset String
    windowDuration string
    delay string
    offset string
    windowDuration String
    delay String
    offset String

    FeatureEngineeringFeatureFunctionAggregationFunctionVarPop, FeatureEngineeringFeatureFunctionAggregationFunctionVarPopArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionAggregationFunctionVarSamp, FeatureEngineeringFeatureFunctionAggregationFunctionVarSampArgs

    Input string
    Input string
    input string
    input String
    input string
    input str
    input String

    FeatureEngineeringFeatureFunctionColumnSelection, FeatureEngineeringFeatureFunctionColumnSelectionArgs

    Column string
    Column string
    column string
    column String
    column string
    column str
    column String

    FeatureEngineeringFeatureFunctionCustomUdf, FeatureEngineeringFeatureFunctionCustomUdfArgs

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

    FeatureEngineeringFeatureFunctionCustomUdfInputBinding, FeatureEngineeringFeatureFunctionCustomUdfInputBindingArgs

    Column string
    Parameter string
    Name of the UC function parameter
    Column string
    Parameter string
    Name of the UC function parameter
    column string
    parameter string
    Name of the UC function parameter
    column String
    parameter String
    Name of the UC function parameter
    column string
    parameter string
    Name of the UC function parameter
    column str
    parameter str
    Name of the UC function parameter
    column String
    parameter String
    Name of the UC function parameter

    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 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
    job_context object
    Job context information including job ID and run ID
    notebook_id number
    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
    job_id number
    The job ID where this API invoked
    job_run_id number
    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

    FeatureEngineeringFeatureProviderConfig, FeatureEngineeringFeatureProviderConfigArgs

    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.

    FeatureEngineeringFeatureSource, FeatureEngineeringFeatureSourceArgs

    DeltaTableSource FeatureEngineeringFeatureSourceDeltaTableSource
    A Delta table data source
    FeatureViewSource FeatureEngineeringFeatureSourceFeatureViewSource
    A data source composed from registered upstream Features
    KafkaSource FeatureEngineeringFeatureSourceKafkaSource
    A Kafka stream data source
    Lateness FeatureEngineeringFeatureSourceLateness
    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 FeatureEngineeringFeatureSourceRequestSource
    A request-time data source
    SdpSource FeatureEngineeringFeatureSourceSdpSource
    A Spark Declarative Pipeline data source
    StreamSource FeatureEngineeringFeatureSourceStreamSource
    A Stream data source
    DeltaTableSource FeatureEngineeringFeatureSourceDeltaTableSource
    A Delta table data source
    FeatureViewSource FeatureEngineeringFeatureSourceFeatureViewSource
    A data source composed from registered upstream Features
    KafkaSource FeatureEngineeringFeatureSourceKafkaSource
    A Kafka stream data source
    Lateness FeatureEngineeringFeatureSourceLateness
    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 FeatureEngineeringFeatureSourceRequestSource
    A request-time data source
    SdpSource FeatureEngineeringFeatureSourceSdpSource
    A Spark Declarative Pipeline data source
    StreamSource FeatureEngineeringFeatureSourceStreamSource
    A Stream data source
    delta_table_source object
    A Delta table data source
    feature_view_source object
    A data source composed from registered upstream Features
    kafka_source object
    A Kafka stream data source
    lateness object
    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
    A request-time data source
    sdp_source object
    A Spark Declarative Pipeline data source
    stream_source object
    A Stream data source
    deltaTableSource FeatureEngineeringFeatureSourceDeltaTableSource
    A Delta table data source
    featureViewSource FeatureEngineeringFeatureSourceFeatureViewSource
    A data source composed from registered upstream Features
    kafkaSource FeatureEngineeringFeatureSourceKafkaSource
    A Kafka stream data source
    lateness FeatureEngineeringFeatureSourceLateness
    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 FeatureEngineeringFeatureSourceRequestSource
    A request-time data source
    sdpSource FeatureEngineeringFeatureSourceSdpSource
    A Spark Declarative Pipeline data source
    streamSource FeatureEngineeringFeatureSourceStreamSource
    A Stream data source
    deltaTableSource FeatureEngineeringFeatureSourceDeltaTableSource
    A Delta table data source
    featureViewSource FeatureEngineeringFeatureSourceFeatureViewSource
    A data source composed from registered upstream Features
    kafkaSource FeatureEngineeringFeatureSourceKafkaSource
    A Kafka stream data source
    lateness FeatureEngineeringFeatureSourceLateness
    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 FeatureEngineeringFeatureSourceRequestSource
    A request-time data source
    sdpSource FeatureEngineeringFeatureSourceSdpSource
    A Spark Declarative Pipeline data source
    streamSource FeatureEngineeringFeatureSourceStreamSource
    A Stream data source
    delta_table_source FeatureEngineeringFeatureSourceDeltaTableSource
    A Delta table data source
    feature_view_source FeatureEngineeringFeatureSourceFeatureViewSource
    A data source composed from registered upstream Features
    kafka_source FeatureEngineeringFeatureSourceKafkaSource
    A Kafka stream data source
    lateness FeatureEngineeringFeatureSourceLateness
    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 FeatureEngineeringFeatureSourceRequestSource
    A request-time data source
    sdp_source FeatureEngineeringFeatureSourceSdpSource
    A Spark Declarative Pipeline data source
    stream_source FeatureEngineeringFeatureSourceStreamSource
    A Stream data source
    deltaTableSource Property Map
    A Delta table data source
    featureViewSource Property Map
    A data source composed from registered upstream Features
    kafkaSource Property Map
    A Kafka stream data source
    lateness Property Map
    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
    A request-time data source
    sdpSource Property Map
    A Spark Declarative Pipeline data source
    streamSource Property Map
    A Stream data source

    FeatureEngineeringFeatureSourceDeltaTableSource, FeatureEngineeringFeatureSourceDeltaTableSourceArgs

    FullName string
    The full three-part name (catalog, schema, name) of the feature
    DataframeSchema string
    EntityColumns List<string>
    FilterCondition string
    TimeseriesColumn string
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    TransformationSql string
    FullName string
    The full three-part name (catalog, schema, name) of the feature
    DataframeSchema string
    EntityColumns []string
    FilterCondition string
    TimeseriesColumn string
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    TransformationSql string
    full_name string
    The full three-part name (catalog, schema, name) of the feature
    dataframe_schema string
    entity_columns list(string)
    filter_condition string
    timeseries_column string
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    transformation_sql string
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    dataframeSchema String
    entityColumns List<String>
    filterCondition String
    timeseriesColumn String
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    transformationSql String
    fullName string
    The full three-part name (catalog, schema, name) of the feature
    dataframeSchema string
    entityColumns string[]
    filterCondition string
    timeseriesColumn string
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    transformationSql string
    full_name str
    The full three-part name (catalog, schema, name) of the feature
    dataframe_schema str
    entity_columns Sequence[str]
    filter_condition str
    timeseries_column str
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    transformation_sql str
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    dataframeSchema String
    entityColumns List<String>
    filterCondition String
    timeseriesColumn String
    Column recording time, used for point-in-time joins, backfills, and aggregations. Optional since a timeseries column is not set for RequestSource features or on-demand calculated features
    transformationSql String

    FeatureEngineeringFeatureSourceFeatureViewSource, FeatureEngineeringFeatureSourceFeatureViewSourceArgs

    FeatureReferences List<FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReference>
    The upstream Features this source reads. Must include at least one feature
    FeatureReferences []FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReference
    The upstream Features this source reads. Must include at least one feature
    feature_references list(object)
    The upstream Features this source reads. Must include at least one feature
    featureReferences List<FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReference>
    The upstream Features this source reads. Must include at least one feature
    featureReferences FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReference[]
    The upstream Features this source reads. Must include at least one feature
    feature_references Sequence[FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReference]
    The upstream Features this source reads. Must include at least one feature
    featureReferences List<Property Map>
    The upstream Features this source reads. Must include at least one feature

    FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReference, FeatureEngineeringFeatureSourceFeatureViewSourceFeatureReferenceArgs

    Feature string
    The three-part full name of the upstream Feature
    Feature string
    The three-part full name of the upstream Feature
    feature string
    The three-part full name of the upstream Feature
    feature String
    The three-part full name of the upstream Feature
    feature string
    The three-part full name of the upstream Feature
    feature str
    The three-part full name of the upstream Feature
    feature String
    The three-part full name of the upstream Feature

    FeatureEngineeringFeatureSourceKafkaSource, FeatureEngineeringFeatureSourceKafkaSourceArgs

    name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    entity_column_identifiers list(object)
    filter_condition string
    timeseries_column_identifier object
    name String
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    entityColumnIdentifiers List<Property Map>
    filterCondition String
    timeseriesColumnIdentifier Property Map

    FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifier, FeatureEngineeringFeatureSourceKafkaSourceEntityColumnIdentifierArgs

    VariantExprPath string
    String representation of the column name using dot-prefixed path notation
    VariantExprPath string
    String representation of the column name using dot-prefixed path notation
    variant_expr_path string
    String representation of the column name using dot-prefixed path notation
    variantExprPath String
    String representation of the column name using dot-prefixed path notation
    variantExprPath string
    String representation of the column name using dot-prefixed path notation
    variant_expr_path str
    String representation of the column name using dot-prefixed path notation
    variantExprPath String
    String representation of the column name using dot-prefixed path notation

    FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifier, FeatureEngineeringFeatureSourceKafkaSourceTimeseriesColumnIdentifierArgs

    VariantExprPath string
    String representation of the column name using dot-prefixed path notation
    VariantExprPath string
    String representation of the column name using dot-prefixed path notation
    variant_expr_path string
    String representation of the column name using dot-prefixed path notation
    variantExprPath String
    String representation of the column name using dot-prefixed path notation
    variantExprPath string
    String representation of the column name using dot-prefixed path notation
    variant_expr_path str
    String representation of the column name using dot-prefixed path notation
    variantExprPath String
    String representation of the column name using dot-prefixed path notation

    FeatureEngineeringFeatureSourceLateness, FeatureEngineeringFeatureSourceLatenessArgs

    SettlingDelay 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
    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
    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
    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
    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
    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
    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

    FeatureEngineeringFeatureSourceRequestSource, FeatureEngineeringFeatureSourceRequestSourceArgs

    dataframe_schema string
    flat_schema object
    A flat schema with scalar-typed fields only
    dataframeSchema String
    flatSchema Property Map
    A flat schema with scalar-typed fields only

    FeatureEngineeringFeatureSourceRequestSourceFlatSchema, FeatureEngineeringFeatureSourceRequestSourceFlatSchemaArgs

    fields list(object)
    The list of fields in this schema
    fields List<Property Map>
    The list of fields in this schema

    FeatureEngineeringFeatureSourceRequestSourceFlatSchemaField, FeatureEngineeringFeatureSourceRequestSourceFlatSchemaFieldArgs

    DataType 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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    DataType 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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    data_type 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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    dataType 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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    dataType 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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    data_type str
    The scalar data type of the field. Possible values are: BINARY, BOOLEAN, DATE, DECIMAL, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIMESTAMP
    name str
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    dataType 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) - Name of the feature, extracted from the full three-part name (catalog.schema.name)

    FeatureEngineeringFeatureSourceSdpSource, FeatureEngineeringFeatureSourceSdpSourceArgs

    DataframeSchema string
    FilterCondition string
    InputTables List<string>
    The full three-part names of the tables the pipeline reads from. Required to materialize this feature with a TableTrigger, which starts a run when these tables update. Each table must exist when the feature is created. After the pipeline runs, Feature Store checks that it reads from these tables, and the run fails if it doesn't. At most 100 tables
    OutputTable string
    The full three-part (catalog, schema, table) name of the pipeline's output table to read from. The table doesn't need to exist until the pipeline first runs. Required
    PipelineId string
    The ID of the Spark Declarative Pipeline that Feature Store runs to produce this source, before each materialization or backfill. The pipeline must already exist when the feature is created, and you need CAN_RUN, CAN_MANAGE, or owner permission on it. It doesn't need to have run yet. Deleting the feature doesn't delete the pipeline. If the pipeline is deleted, this field reads back empty. Required
    PipelineState string
    (string) - State tracking pipeline execution and output table validation. PENDING until the pipeline has run at least once and created the output table; then RESOLVED on success, or ERROR on failure. Possible values are: ERROR, PENDING, RESOLVED
    TransformationSql string
    DataframeSchema string
    FilterCondition string
    InputTables []string
    The full three-part names of the tables the pipeline reads from. Required to materialize this feature with a TableTrigger, which starts a run when these tables update. Each table must exist when the feature is created. After the pipeline runs, Feature Store checks that it reads from these tables, and the run fails if it doesn't. At most 100 tables
    OutputTable string
    The full three-part (catalog, schema, table) name of the pipeline's output table to read from. The table doesn't need to exist until the pipeline first runs. Required
    PipelineId string
    The ID of the Spark Declarative Pipeline that Feature Store runs to produce this source, before each materialization or backfill. The pipeline must already exist when the feature is created, and you need CAN_RUN, CAN_MANAGE, or owner permission on it. It doesn't need to have run yet. Deleting the feature doesn't delete the pipeline. If the pipeline is deleted, this field reads back empty. Required
    PipelineState string
    (string) - State tracking pipeline execution and output table validation. PENDING until the pipeline has run at least once and created the output table; then RESOLVED on success, or ERROR on failure. Possible values are: ERROR, PENDING, RESOLVED
    TransformationSql string
    dataframe_schema string
    filter_condition string
    input_tables list(string)
    The full three-part names of the tables the pipeline reads from. Required to materialize this feature with a TableTrigger, which starts a run when these tables update. Each table must exist when the feature is created. After the pipeline runs, Feature Store checks that it reads from these tables, and the run fails if it doesn't. At most 100 tables
    output_table string
    The full three-part (catalog, schema, table) name of the pipeline's output table to read from. The table doesn't need to exist until the pipeline first runs. Required
    pipeline_id string
    The ID of the Spark Declarative Pipeline that Feature Store runs to produce this source, before each materialization or backfill. The pipeline must already exist when the feature is created, and you need CAN_RUN, CAN_MANAGE, or owner permission on it. It doesn't need to have run yet. Deleting the feature doesn't delete the pipeline. If the pipeline is deleted, this field reads back empty. Required
    pipeline_state string
    (string) - State tracking pipeline execution and output table validation. PENDING until the pipeline has run at least once and created the output table; then RESOLVED on success, or ERROR on failure. Possible values are: ERROR, PENDING, RESOLVED
    transformation_sql string
    dataframeSchema String
    filterCondition String
    inputTables List<String>
    The full three-part names of the tables the pipeline reads from. Required to materialize this feature with a TableTrigger, which starts a run when these tables update. Each table must exist when the feature is created. After the pipeline runs, Feature Store checks that it reads from these tables, and the run fails if it doesn't. At most 100 tables
    outputTable String
    The full three-part (catalog, schema, table) name of the pipeline's output table to read from. The table doesn't need to exist until the pipeline first runs. Required
    pipelineId String
    The ID of the Spark Declarative Pipeline that Feature Store runs to produce this source, before each materialization or backfill. The pipeline must already exist when the feature is created, and you need CAN_RUN, CAN_MANAGE, or owner permission on it. It doesn't need to have run yet. Deleting the feature doesn't delete the pipeline. If the pipeline is deleted, this field reads back empty. Required
    pipelineState String
    (string) - State tracking pipeline execution and output table validation. PENDING until the pipeline has run at least once and created the output table; then RESOLVED on success, or ERROR on failure. Possible values are: ERROR, PENDING, RESOLVED
    transformationSql String
    dataframeSchema string
    filterCondition string
    inputTables string[]
    The full three-part names of the tables the pipeline reads from. Required to materialize this feature with a TableTrigger, which starts a run when these tables update. Each table must exist when the feature is created. After the pipeline runs, Feature Store checks that it reads from these tables, and the run fails if it doesn't. At most 100 tables
    outputTable string
    The full three-part (catalog, schema, table) name of the pipeline's output table to read from. The table doesn't need to exist until the pipeline first runs. Required
    pipelineId string
    The ID of the Spark Declarative Pipeline that Feature Store runs to produce this source, before each materialization or backfill. The pipeline must already exist when the feature is created, and you need CAN_RUN, CAN_MANAGE, or owner permission on it. It doesn't need to have run yet. Deleting the feature doesn't delete the pipeline. If the pipeline is deleted, this field reads back empty. Required
    pipelineState string
    (string) - State tracking pipeline execution and output table validation. PENDING until the pipeline has run at least once and created the output table; then RESOLVED on success, or ERROR on failure. Possible values are: ERROR, PENDING, RESOLVED
    transformationSql string
    dataframe_schema str
    filter_condition str
    input_tables Sequence[str]
    The full three-part names of the tables the pipeline reads from. Required to materialize this feature with a TableTrigger, which starts a run when these tables update. Each table must exist when the feature is created. After the pipeline runs, Feature Store checks that it reads from these tables, and the run fails if it doesn't. At most 100 tables
    output_table str
    The full three-part (catalog, schema, table) name of the pipeline's output table to read from. The table doesn't need to exist until the pipeline first runs. Required
    pipeline_id str
    The ID of the Spark Declarative Pipeline that Feature Store runs to produce this source, before each materialization or backfill. The pipeline must already exist when the feature is created, and you need CAN_RUN, CAN_MANAGE, or owner permission on it. It doesn't need to have run yet. Deleting the feature doesn't delete the pipeline. If the pipeline is deleted, this field reads back empty. Required
    pipeline_state str
    (string) - State tracking pipeline execution and output table validation. PENDING until the pipeline has run at least once and created the output table; then RESOLVED on success, or ERROR on failure. Possible values are: ERROR, PENDING, RESOLVED
    transformation_sql str
    dataframeSchema String
    filterCondition String
    inputTables List<String>
    The full three-part names of the tables the pipeline reads from. Required to materialize this feature with a TableTrigger, which starts a run when these tables update. Each table must exist when the feature is created. After the pipeline runs, Feature Store checks that it reads from these tables, and the run fails if it doesn't. At most 100 tables
    outputTable String
    The full three-part (catalog, schema, table) name of the pipeline's output table to read from. The table doesn't need to exist until the pipeline first runs. Required
    pipelineId String
    The ID of the Spark Declarative Pipeline that Feature Store runs to produce this source, before each materialization or backfill. The pipeline must already exist when the feature is created, and you need CAN_RUN, CAN_MANAGE, or owner permission on it. It doesn't need to have run yet. Deleting the feature doesn't delete the pipeline. If the pipeline is deleted, this field reads back empty. Required
    pipelineState String
    (string) - State tracking pipeline execution and output table validation. PENDING until the pipeline has run at least once and created the output table; then RESOLVED on success, or ERROR on failure. Possible values are: ERROR, PENDING, RESOLVED
    transformationSql String

    FeatureEngineeringFeatureSourceStreamSource, FeatureEngineeringFeatureSourceStreamSourceArgs

    FullName string
    The full three-part name (catalog, schema, name) of the feature
    DataframeSchema string
    FilterCondition string
    TransformationSql string
    FullName string
    The full three-part name (catalog, schema, name) of the feature
    DataframeSchema string
    FilterCondition string
    TransformationSql string
    full_name string
    The full three-part name (catalog, schema, name) of the feature
    dataframe_schema string
    filter_condition string
    transformation_sql string
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    dataframeSchema String
    filterCondition String
    transformationSql String
    fullName string
    The full three-part name (catalog, schema, name) of the feature
    dataframeSchema string
    filterCondition string
    transformationSql string
    full_name str
    The full three-part name (catalog, schema, name) of the feature
    dataframe_schema str
    filter_condition str
    transformation_sql str
    fullName String
    The full three-part name (catalog, schema, name) of the feature
    dataframeSchema String
    filterCondition String
    transformationSql String

    FeatureEngineeringFeatureTimeWindow, FeatureEngineeringFeatureTimeWindowArgs

    Continuous FeatureEngineeringFeatureTimeWindowContinuous
    Rolling FeatureEngineeringFeatureTimeWindowRolling
    Sawtooth FeatureEngineeringFeatureTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    Sliding FeatureEngineeringFeatureTimeWindowSliding
    StartTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    Tumbling FeatureEngineeringFeatureTimeWindowTumbling
    Continuous FeatureEngineeringFeatureTimeWindowContinuous
    Rolling FeatureEngineeringFeatureTimeWindowRolling
    Sawtooth FeatureEngineeringFeatureTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    Sliding FeatureEngineeringFeatureTimeWindowSliding
    StartTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    Tumbling FeatureEngineeringFeatureTimeWindowTumbling
    continuous object
    rolling object
    sawtooth object
    A sawtooth window served via the hybrid batch + streaming path
    sliding object
    start_time 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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling object
    continuous FeatureEngineeringFeatureTimeWindowContinuous
    rolling FeatureEngineeringFeatureTimeWindowRolling
    sawtooth FeatureEngineeringFeatureTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    sliding FeatureEngineeringFeatureTimeWindowSliding
    startTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling FeatureEngineeringFeatureTimeWindowTumbling
    continuous FeatureEngineeringFeatureTimeWindowContinuous
    rolling FeatureEngineeringFeatureTimeWindowRolling
    sawtooth FeatureEngineeringFeatureTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    sliding FeatureEngineeringFeatureTimeWindowSliding
    startTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling FeatureEngineeringFeatureTimeWindowTumbling
    continuous FeatureEngineeringFeatureTimeWindowContinuous
    rolling FeatureEngineeringFeatureTimeWindowRolling
    sawtooth FeatureEngineeringFeatureTimeWindowSawtooth
    A sawtooth window served via the hybrid batch + streaming path
    sliding FeatureEngineeringFeatureTimeWindowSliding
    start_time str
    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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling FeatureEngineeringFeatureTimeWindowTumbling
    continuous Property Map
    rolling Property Map
    sawtooth Property Map
    A sawtooth window served via the hybrid batch + streaming path
    sliding Property Map
    startTime 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. Not currently supported for sawtooth windows or for Features with a stream source
    tumbling Property Map

    FeatureEngineeringFeatureTimeWindowContinuous, FeatureEngineeringFeatureTimeWindowContinuousArgs

    WindowDuration string
    Offset string
    WindowDuration string
    Offset string
    window_duration string
    offset string
    windowDuration String
    offset String
    windowDuration string
    offset string
    windowDuration String
    offset String

    FeatureEngineeringFeatureTimeWindowRolling, FeatureEngineeringFeatureTimeWindowRollingArgs

    Delay string
    WindowDuration string
    Delay string
    WindowDuration string
    delay string
    window_duration string
    delay String
    windowDuration String
    delay string
    windowDuration string
    delay String
    windowDuration String

    FeatureEngineeringFeatureTimeWindowSawtooth, FeatureEngineeringFeatureTimeWindowSawtoothArgs

    Delay string
    WindowDuration string
    Delay string
    WindowDuration string
    delay string
    window_duration string
    delay String
    windowDuration String
    delay string
    windowDuration string
    delay String
    windowDuration String

    FeatureEngineeringFeatureTimeWindowSliding, FeatureEngineeringFeatureTimeWindowSlidingArgs

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

    FeatureEngineeringFeatureTimeWindowTumbling, FeatureEngineeringFeatureTimeWindowTumblingArgs

    WindowDuration string
    Delay string
    Offset string
    WindowDuration string
    Delay string
    Offset string
    window_duration string
    delay string
    offset string
    windowDuration String
    delay String
    offset String
    windowDuration string
    delay string
    offset string
    windowDuration String
    delay String
    offset String

    FeatureEngineeringFeatureTimeseriesColumn, FeatureEngineeringFeatureTimeseriesColumnArgs

    Name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    Name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name String
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name string
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name str
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)
    name String
    (string) - Name of the feature, extracted from the full three-part name (catalog.schema.name)

    Package Details

    Repository
    databricks pulumi/pulumi-databricks
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the databricks Terraform Provider.
    databricks logo databricks logo
    Viewing docs for Databricks v1.118.1
    published on Tuesday, Oct 27, 2026 by Pulumi
    Schema (JSON)
    pulumi/pulumi-databricks

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