databricks.getFeatureEngineeringKafkaConfig

Databricks v1.90.0, Mar 19 26

Viewing docs for Databricks v1.90.0
published on Thursday, Mar 19, 2026 by Pulumi

Schema (JSON)

pulumi/pulumi-databricks

Viewing docs for Databricks v1.90.0
published on Thursday, Mar 19, 2026 by Pulumi

Schema (JSON)

pulumi/pulumi-databricks

Using getFeatureEngineeringKafkaConfig

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

function getFeatureEngineeringKafkaConfig(args: GetFeatureEngineeringKafkaConfigArgs, opts?: InvokeOptions): Promise<GetFeatureEngineeringKafkaConfigResult>
function getFeatureEngineeringKafkaConfigOutput(args: GetFeatureEngineeringKafkaConfigOutputArgs, opts?: InvokeOptions): Output<GetFeatureEngineeringKafkaConfigResult>

def get_feature_engineering_kafka_config(name: Optional[str] = None,
                                         provider_config: Optional[GetFeatureEngineeringKafkaConfigProviderConfig] = None,
                                         opts: Optional[InvokeOptions] = None) -> GetFeatureEngineeringKafkaConfigResult
def get_feature_engineering_kafka_config_output(name: Optional[pulumi.Input[str]] = None,
                                         provider_config: Optional[pulumi.Input[GetFeatureEngineeringKafkaConfigProviderConfigArgs]] = None,
                                         opts: Optional[InvokeOptions] = None) -> Output[GetFeatureEngineeringKafkaConfigResult]

func LookupFeatureEngineeringKafkaConfig(ctx *Context, args *LookupFeatureEngineeringKafkaConfigArgs, opts ...InvokeOption) (*LookupFeatureEngineeringKafkaConfigResult, error)
func LookupFeatureEngineeringKafkaConfigOutput(ctx *Context, args *LookupFeatureEngineeringKafkaConfigOutputArgs, opts ...InvokeOption) LookupFeatureEngineeringKafkaConfigResultOutput

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

public static class GetFeatureEngineeringKafkaConfig 
{
    public static Task<GetFeatureEngineeringKafkaConfigResult> InvokeAsync(GetFeatureEngineeringKafkaConfigArgs args, InvokeOptions? opts = null)
    public static Output<GetFeatureEngineeringKafkaConfigResult> Invoke(GetFeatureEngineeringKafkaConfigInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetFeatureEngineeringKafkaConfigResult> getFeatureEngineeringKafkaConfig(GetFeatureEngineeringKafkaConfigArgs args, InvokeOptions options)
public static Output<GetFeatureEngineeringKafkaConfigResult> getFeatureEngineeringKafkaConfig(GetFeatureEngineeringKafkaConfigArgs args, InvokeOptions options)

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

The following arguments are supported:

Name string: Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
ProviderConfig GetFeatureEngineeringKafkaConfigProviderConfig: Configure the provider for management through account provider.

Name string: Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
ProviderConfig GetFeatureEngineeringKafkaConfigProviderConfig: Configure the provider for management through account provider.

name String: Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
providerConfig GetFeatureEngineeringKafkaConfigProviderConfig: Configure the provider for management through account provider.

name string: Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
providerConfig GetFeatureEngineeringKafkaConfigProviderConfig: Configure the provider for management through account provider.

name str: Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
provider_config GetFeatureEngineeringKafkaConfigProviderConfig: Configure the provider for management through account provider.

name String: Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
providerConfig Property Map: Configure the provider for management through account provider.

getFeatureEngineeringKafkaConfig Result

The following output properties are available:

AuthConfig GetFeatureEngineeringKafkaConfigAuthConfig: (AuthConfig) - Authentication configuration for connection to topics
BackfillSource GetFeatureEngineeringKafkaConfigBackfillSource: (BackfillSource) - A user-provided and managed source for backfilling data. Historical data is used when creating a training set from streaming features linked to this Kafka config. In the future, a separate table will be maintained by Databricks for forward filling data. The schema for this source must match exactly that of the key and value schemas specified for this Kafka config
BootstrapServers string: (string) - A comma-separated list of host/port pairs pointing to Kafka cluster
ExtraOptions Dictionary<string, string>: (object) - Catch-all for miscellaneous options. Keys should be source options or Kafka consumer options (kafka.*)
Id string: The provider-assigned unique ID for this managed resource.
KeySchema GetFeatureEngineeringKafkaConfigKeySchema: (SchemaConfig) - Schema configuration for extracting message keys from topics. At least one of key_schema and value_schema must be provided
Name string: (string) - Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
SubscriptionMode GetFeatureEngineeringKafkaConfigSubscriptionMode: (SubscriptionMode) - Options to configure which Kafka topics to pull data from
ValueSchema GetFeatureEngineeringKafkaConfigValueSchema: (SchemaConfig) - Schema configuration for extracting message values from topics. At least one of key_schema and value_schema must be provided
ProviderConfig GetFeatureEngineeringKafkaConfigProviderConfig

AuthConfig GetFeatureEngineeringKafkaConfigAuthConfig: (AuthConfig) - Authentication configuration for connection to topics
BackfillSource GetFeatureEngineeringKafkaConfigBackfillSource: (BackfillSource) - A user-provided and managed source for backfilling data. Historical data is used when creating a training set from streaming features linked to this Kafka config. In the future, a separate table will be maintained by Databricks for forward filling data. The schema for this source must match exactly that of the key and value schemas specified for this Kafka config
BootstrapServers string: (string) - A comma-separated list of host/port pairs pointing to Kafka cluster
ExtraOptions map[string]string: (object) - Catch-all for miscellaneous options. Keys should be source options or Kafka consumer options (kafka.*)
Id string: The provider-assigned unique ID for this managed resource.
KeySchema GetFeatureEngineeringKafkaConfigKeySchema: (SchemaConfig) - Schema configuration for extracting message keys from topics. At least one of key_schema and value_schema must be provided
Name string: (string) - Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
SubscriptionMode GetFeatureEngineeringKafkaConfigSubscriptionMode: (SubscriptionMode) - Options to configure which Kafka topics to pull data from
ValueSchema GetFeatureEngineeringKafkaConfigValueSchema: (SchemaConfig) - Schema configuration for extracting message values from topics. At least one of key_schema and value_schema must be provided
ProviderConfig GetFeatureEngineeringKafkaConfigProviderConfig

authConfig GetFeatureEngineeringKafkaConfigAuthConfig: (AuthConfig) - Authentication configuration for connection to topics
backfillSource GetFeatureEngineeringKafkaConfigBackfillSource: (BackfillSource) - A user-provided and managed source for backfilling data. Historical data is used when creating a training set from streaming features linked to this Kafka config. In the future, a separate table will be maintained by Databricks for forward filling data. The schema for this source must match exactly that of the key and value schemas specified for this Kafka config
bootstrapServers String: (string) - A comma-separated list of host/port pairs pointing to Kafka cluster
extraOptions Map<String,String>: (object) - Catch-all for miscellaneous options. Keys should be source options or Kafka consumer options (kafka.*)
id String: The provider-assigned unique ID for this managed resource.
keySchema GetFeatureEngineeringKafkaConfigKeySchema: (SchemaConfig) - Schema configuration for extracting message keys from topics. At least one of key_schema and value_schema must be provided
name String: (string) - Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
subscriptionMode GetFeatureEngineeringKafkaConfigSubscriptionMode: (SubscriptionMode) - Options to configure which Kafka topics to pull data from
valueSchema GetFeatureEngineeringKafkaConfigValueSchema: (SchemaConfig) - Schema configuration for extracting message values from topics. At least one of key_schema and value_schema must be provided
providerConfig GetFeatureEngineeringKafkaConfigProviderConfig

authConfig GetFeatureEngineeringKafkaConfigAuthConfig: (AuthConfig) - Authentication configuration for connection to topics
backfillSource GetFeatureEngineeringKafkaConfigBackfillSource: (BackfillSource) - A user-provided and managed source for backfilling data. Historical data is used when creating a training set from streaming features linked to this Kafka config. In the future, a separate table will be maintained by Databricks for forward filling data. The schema for this source must match exactly that of the key and value schemas specified for this Kafka config
bootstrapServers string: (string) - A comma-separated list of host/port pairs pointing to Kafka cluster
extraOptions {[key: string]: string}: (object) - Catch-all for miscellaneous options. Keys should be source options or Kafka consumer options (kafka.*)
id string: The provider-assigned unique ID for this managed resource.
keySchema GetFeatureEngineeringKafkaConfigKeySchema: (SchemaConfig) - Schema configuration for extracting message keys from topics. At least one of key_schema and value_schema must be provided
name string: (string) - Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
subscriptionMode GetFeatureEngineeringKafkaConfigSubscriptionMode: (SubscriptionMode) - Options to configure which Kafka topics to pull data from
valueSchema GetFeatureEngineeringKafkaConfigValueSchema: (SchemaConfig) - Schema configuration for extracting message values from topics. At least one of key_schema and value_schema must be provided
providerConfig GetFeatureEngineeringKafkaConfigProviderConfig

auth_config GetFeatureEngineeringKafkaConfigAuthConfig: (AuthConfig) - Authentication configuration for connection to topics
backfill_source GetFeatureEngineeringKafkaConfigBackfillSource: (BackfillSource) - A user-provided and managed source for backfilling data. Historical data is used when creating a training set from streaming features linked to this Kafka config. In the future, a separate table will be maintained by Databricks for forward filling data. The schema for this source must match exactly that of the key and value schemas specified for this Kafka config
bootstrap_servers str: (string) - A comma-separated list of host/port pairs pointing to Kafka cluster
extra_options Mapping[str, str]: (object) - Catch-all for miscellaneous options. Keys should be source options or Kafka consumer options (kafka.*)
id str: The provider-assigned unique ID for this managed resource.
key_schema GetFeatureEngineeringKafkaConfigKeySchema: (SchemaConfig) - Schema configuration for extracting message keys from topics. At least one of key_schema and value_schema must be provided
name str: (string) - Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
subscription_mode GetFeatureEngineeringKafkaConfigSubscriptionMode: (SubscriptionMode) - Options to configure which Kafka topics to pull data from
value_schema GetFeatureEngineeringKafkaConfigValueSchema: (SchemaConfig) - Schema configuration for extracting message values from topics. At least one of key_schema and value_schema must be provided
provider_config GetFeatureEngineeringKafkaConfigProviderConfig

authConfig Property Map: (AuthConfig) - Authentication configuration for connection to topics
backfillSource Property Map: (BackfillSource) - A user-provided and managed source for backfilling data. Historical data is used when creating a training set from streaming features linked to this Kafka config. In the future, a separate table will be maintained by Databricks for forward filling data. The schema for this source must match exactly that of the key and value schemas specified for this Kafka config
bootstrapServers String: (string) - A comma-separated list of host/port pairs pointing to Kafka cluster
extraOptions Map<String>: (object) - Catch-all for miscellaneous options. Keys should be source options or Kafka consumer options (kafka.*)
id String: The provider-assigned unique ID for this managed resource.
keySchema Property Map: (SchemaConfig) - Schema configuration for extracting message keys from topics. At least one of key_schema and value_schema must be provided
name String: (string) - Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name
subscriptionMode Property Map: (SubscriptionMode) - Options to configure which Kafka topics to pull data from
valueSchema Property Map: (SchemaConfig) - Schema configuration for extracting message values from topics. At least one of key_schema and value_schema must be provided
providerConfig Property Map

Supporting Types

GetFeatureEngineeringKafkaConfigAuthConfig

UcServiceCredentialName string: (string) - Name of the Unity Catalog service credential. This value will be set under the option databricks.serviceCredential

UcServiceCredentialName string: (string) - Name of the Unity Catalog service credential. This value will be set under the option databricks.serviceCredential

ucServiceCredentialName String: (string) - Name of the Unity Catalog service credential. This value will be set under the option databricks.serviceCredential

ucServiceCredentialName string: (string) - Name of the Unity Catalog service credential. This value will be set under the option databricks.serviceCredential

uc_service_credential_name str: (string) - Name of the Unity Catalog service credential. This value will be set under the option databricks.serviceCredential

ucServiceCredentialName String: (string) - Name of the Unity Catalog service credential. This value will be set under the option databricks.serviceCredential

GetFeatureEngineeringKafkaConfigBackfillSource

DeltaTableSource GetFeatureEngineeringKafkaConfigBackfillSourceDeltaTableSource: (DeltaTableSource) - The Delta table source containing the historic data to backfill. Only the delta table name is used for backfill, the entity columns and timeseries column are ignored as they are defined by the associated KafkaSource

DeltaTableSource GetFeatureEngineeringKafkaConfigBackfillSourceDeltaTableSource: (DeltaTableSource) - The Delta table source containing the historic data to backfill. Only the delta table name is used for backfill, the entity columns and timeseries column are ignored as they are defined by the associated KafkaSource

deltaTableSource GetFeatureEngineeringKafkaConfigBackfillSourceDeltaTableSource: (DeltaTableSource) - The Delta table source containing the historic data to backfill. Only the delta table name is used for backfill, the entity columns and timeseries column are ignored as they are defined by the associated KafkaSource

deltaTableSource GetFeatureEngineeringKafkaConfigBackfillSourceDeltaTableSource: (DeltaTableSource) - The Delta table source containing the historic data to backfill. Only the delta table name is used for backfill, the entity columns and timeseries column are ignored as they are defined by the associated KafkaSource

delta_table_source GetFeatureEngineeringKafkaConfigBackfillSourceDeltaTableSource: (DeltaTableSource) - The Delta table source containing the historic data to backfill. Only the delta table name is used for backfill, the entity columns and timeseries column are ignored as they are defined by the associated KafkaSource

deltaTableSource Property Map: (DeltaTableSource) - The Delta table source containing the historic data to backfill. Only the delta table name is used for backfill, the entity columns and timeseries column are ignored as they are defined by the associated KafkaSource

GetFeatureEngineeringKafkaConfigBackfillSourceDeltaTableSource

FullName string: (string) - The full three-part (catalog, schema, table) name of the Delta table
DataframeSchema string: (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Required if transformation_sql is specified. Example: {"type":"struct","fields":[{"name":"col_a","type":"integer","nullable":true,"metadata":{}},{"name":"col_c","type":"integer","nullable":true,"metadata":{}}]}
EntityColumns List<string>: (list of string, deprecated) - Deprecated: Use Feature.entity instead. Kept for backwards compatibility. The entity columns of the Delta table
FilterCondition string: (string) - Single WHERE clause to filter delta table before applying transformations. Will be row-wise evaluated, so should only include conditionals and projections
TimeseriesColumn string: (string, deprecated) - Deprecated: Use Feature.timeseries_column instead. Kept for backwards compatibility. The timeseries column of the Delta table
TransformationSql string: (string) - A single SQL SELECT expression applied after filter_condition. Should contains all the columns needed (eg. "SELECT , col_a + col_b AS col_c FROM x.y.z WHERE col_a > 0" would have transformation_sql ", col_a + col_b AS col_c") If transformation_sql is not provided, all columns of the delta table are present in the DataSource dataframe

FullName string: (string) - The full three-part (catalog, schema, table) name of the Delta table
DataframeSchema string: (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Required if transformation_sql is specified. Example: {"type":"struct","fields":[{"name":"col_a","type":"integer","nullable":true,"metadata":{}},{"name":"col_c","type":"integer","nullable":true,"metadata":{}}]}
EntityColumns []string: (list of string, deprecated) - Deprecated: Use Feature.entity instead. Kept for backwards compatibility. The entity columns of the Delta table
FilterCondition string: (string) - Single WHERE clause to filter delta table before applying transformations. Will be row-wise evaluated, so should only include conditionals and projections
TimeseriesColumn string: (string, deprecated) - Deprecated: Use Feature.timeseries_column instead. Kept for backwards compatibility. The timeseries column of the Delta table
TransformationSql string: (string) - A single SQL SELECT expression applied after filter_condition. Should contains all the columns needed (eg. "SELECT , col_a + col_b AS col_c FROM x.y.z WHERE col_a > 0" would have transformation_sql ", col_a + col_b AS col_c") If transformation_sql is not provided, all columns of the delta table are present in the DataSource dataframe

fullName String: (string) - The full three-part (catalog, schema, table) name of the Delta table
dataframeSchema String: (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Required if transformation_sql is specified. Example: {"type":"struct","fields":[{"name":"col_a","type":"integer","nullable":true,"metadata":{}},{"name":"col_c","type":"integer","nullable":true,"metadata":{}}]}
entityColumns List<String>: (list of string, deprecated) - Deprecated: Use Feature.entity instead. Kept for backwards compatibility. The entity columns of the Delta table
filterCondition String: (string) - Single WHERE clause to filter delta table before applying transformations. Will be row-wise evaluated, so should only include conditionals and projections
timeseriesColumn String: (string, deprecated) - Deprecated: Use Feature.timeseries_column instead. Kept for backwards compatibility. The timeseries column of the Delta table
transformationSql String: (string) - A single SQL SELECT expression applied after filter_condition. Should contains all the columns needed (eg. "SELECT , col_a + col_b AS col_c FROM x.y.z WHERE col_a > 0" would have transformation_sql ", col_a + col_b AS col_c") If transformation_sql is not provided, all columns of the delta table are present in the DataSource dataframe

fullName string: (string) - The full three-part (catalog, schema, table) name of the Delta table
dataframeSchema string: (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Required if transformation_sql is specified. Example: {"type":"struct","fields":[{"name":"col_a","type":"integer","nullable":true,"metadata":{}},{"name":"col_c","type":"integer","nullable":true,"metadata":{}}]}
entityColumns string[]: (list of string, deprecated) - Deprecated: Use Feature.entity instead. Kept for backwards compatibility. The entity columns of the Delta table
filterCondition string: (string) - Single WHERE clause to filter delta table before applying transformations. Will be row-wise evaluated, so should only include conditionals and projections
timeseriesColumn string: (string, deprecated) - Deprecated: Use Feature.timeseries_column instead. Kept for backwards compatibility. The timeseries column of the Delta table
transformationSql string: (string) - A single SQL SELECT expression applied after filter_condition. Should contains all the columns needed (eg. "SELECT , col_a + col_b AS col_c FROM x.y.z WHERE col_a > 0" would have transformation_sql ", col_a + col_b AS col_c") If transformation_sql is not provided, all columns of the delta table are present in the DataSource dataframe

full_name str: (string) - The full three-part (catalog, schema, table) name of the Delta table
dataframe_schema str: (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Required if transformation_sql is specified. Example: {"type":"struct","fields":[{"name":"col_a","type":"integer","nullable":true,"metadata":{}},{"name":"col_c","type":"integer","nullable":true,"metadata":{}}]}
entity_columns Sequence[str]: (list of string, deprecated) - Deprecated: Use Feature.entity instead. Kept for backwards compatibility. The entity columns of the Delta table
filter_condition str: (string) - Single WHERE clause to filter delta table before applying transformations. Will be row-wise evaluated, so should only include conditionals and projections
timeseries_column str: (string, deprecated) - Deprecated: Use Feature.timeseries_column instead. Kept for backwards compatibility. The timeseries column of the Delta table
transformation_sql str: (string) - A single SQL SELECT expression applied after filter_condition. Should contains all the columns needed (eg. "SELECT , col_a + col_b AS col_c FROM x.y.z WHERE col_a > 0" would have transformation_sql ", col_a + col_b AS col_c") If transformation_sql is not provided, all columns of the delta table are present in the DataSource dataframe

fullName String: (string) - The full three-part (catalog, schema, table) name of the Delta table
dataframeSchema String: (string) - Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Required if transformation_sql is specified. Example: {"type":"struct","fields":[{"name":"col_a","type":"integer","nullable":true,"metadata":{}},{"name":"col_c","type":"integer","nullable":true,"metadata":{}}]}
entityColumns List<String>: (list of string, deprecated) - Deprecated: Use Feature.entity instead. Kept for backwards compatibility. The entity columns of the Delta table
filterCondition String: (string) - Single WHERE clause to filter delta table before applying transformations. Will be row-wise evaluated, so should only include conditionals and projections
timeseriesColumn String: (string, deprecated) - Deprecated: Use Feature.timeseries_column instead. Kept for backwards compatibility. The timeseries column of the Delta table
transformationSql String: (string) - A single SQL SELECT expression applied after filter_condition. Should contains all the columns needed (eg. "SELECT , col_a + col_b AS col_c FROM x.y.z WHERE col_a > 0" would have transformation_sql ", col_a + col_b AS col_c") If transformation_sql is not provided, all columns of the delta table are present in the DataSource dataframe

GetFeatureEngineeringKafkaConfigKeySchema

JsonSchema string: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

JsonSchema string: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

jsonSchema String: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

jsonSchema string: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

json_schema str: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

jsonSchema String: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

GetFeatureEngineeringKafkaConfigProviderConfig

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.

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.

GetFeatureEngineeringKafkaConfigSubscriptionMode

Assign string: (string) - A JSON string that contains the specific topic-partitions to consume from. For example, for '{"topicA":[0,1],"topicB":[2,4]}', topicA's 0'th and 1st partitions will be consumed from
Subscribe string: (string) - A comma-separated list of Kafka topics to read from. For example, 'topicA,topicB,topicC'
SubscribePattern string: (string) - A regular expression matching topics to subscribe to. For example, 'topic.*' will subscribe to all topics starting with 'topic'

Assign string: (string) - A JSON string that contains the specific topic-partitions to consume from. For example, for '{"topicA":[0,1],"topicB":[2,4]}', topicA's 0'th and 1st partitions will be consumed from
Subscribe string: (string) - A comma-separated list of Kafka topics to read from. For example, 'topicA,topicB,topicC'
SubscribePattern string: (string) - A regular expression matching topics to subscribe to. For example, 'topic.*' will subscribe to all topics starting with 'topic'

assign String: (string) - A JSON string that contains the specific topic-partitions to consume from. For example, for '{"topicA":[0,1],"topicB":[2,4]}', topicA's 0'th and 1st partitions will be consumed from
subscribe String: (string) - A comma-separated list of Kafka topics to read from. For example, 'topicA,topicB,topicC'
subscribePattern String: (string) - A regular expression matching topics to subscribe to. For example, 'topic.*' will subscribe to all topics starting with 'topic'

assign string: (string) - A JSON string that contains the specific topic-partitions to consume from. For example, for '{"topicA":[0,1],"topicB":[2,4]}', topicA's 0'th and 1st partitions will be consumed from
subscribe string: (string) - A comma-separated list of Kafka topics to read from. For example, 'topicA,topicB,topicC'
subscribePattern string: (string) - A regular expression matching topics to subscribe to. For example, 'topic.*' will subscribe to all topics starting with 'topic'

assign str: (string) - A JSON string that contains the specific topic-partitions to consume from. For example, for '{"topicA":[0,1],"topicB":[2,4]}', topicA's 0'th and 1st partitions will be consumed from
subscribe str: (string) - A comma-separated list of Kafka topics to read from. For example, 'topicA,topicB,topicC'
subscribe_pattern str: (string) - A regular expression matching topics to subscribe to. For example, 'topic.*' will subscribe to all topics starting with 'topic'

assign String: (string) - A JSON string that contains the specific topic-partitions to consume from. For example, for '{"topicA":[0,1],"topicB":[2,4]}', topicA's 0'th and 1st partitions will be consumed from
subscribe String: (string) - A comma-separated list of Kafka topics to read from. For example, 'topicA,topicB,topicC'
subscribePattern String: (string) - A regular expression matching topics to subscribe to. For example, 'topic.*' will subscribe to all topics starting with 'topic'

GetFeatureEngineeringKafkaConfigValueSchema

JsonSchema string: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

JsonSchema string: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

jsonSchema String: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

jsonSchema string: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

json_schema str: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

jsonSchema String: (string) - Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/)

Package Details

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

Viewing docs for Databricks v1.90.0
published on Thursday, Mar 19, 2026 by Pulumi

Schema (JSON)

pulumi/pulumi-databricks

databricks.getFeatureEngineeringKafkaConfig

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Using getFeatureEngineeringKafkaConfig

getFeatureEngineeringKafkaConfig Result

Supporting Types

GetFeatureEngineeringKafkaConfigAuthConfig

GetFeatureEngineeringKafkaConfigBackfillSource

GetFeatureEngineeringKafkaConfigBackfillSourceDeltaTableSource

GetFeatureEngineeringKafkaConfigKeySchema

GetFeatureEngineeringKafkaConfigProviderConfig

GetFeatureEngineeringKafkaConfigSubscriptionMode

GetFeatureEngineeringKafkaConfigValueSchema

Package Details

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