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Databricks v1.78.0 published on Friday, Nov 7, 2025 by Pulumi
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pulumi/pulumi-databricks

databricks.getDataQualityMonitors

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Databricks v1.78.0 published on Friday, Nov 7, 2025 by Pulumi
Schema (JSON)
pulumi/pulumi-databricks

    Public Beta

    This data source can be used to fetch the list of data quality monitors.

    For the table object_type, the caller must either:

    1. be an owner of the table’s parent catalog
    2. have USE_CATALOG on the table’s parent catalog and be an owner of the table’s parent schema.
    3. have the following permissions:
      • USE_CATALOG on the table’s parent catalog
      • USE_SCHEMA on the table’s parent schema
      • SELECT privilege on the table.

    Note This data source can only be used with a workspace-level provider!

    Example Usage

    Getting a list of all data quality monitors:

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

const all = databricks.getDataQualityMonitors({});

    import pulumi
import pulumi_databricks as databricks

all = databricks.get_data_quality_monitors()

    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.GetDataQualityMonitors(ctx, &databricks.GetDataQualityMonitorsArgs{}, nil)
		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 all = Databricks.GetDataQualityMonitors.Invoke();

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetDataQualityMonitorsArgs;
import java.util.List;
import java.util.ArrayList;
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) {
        final var all = DatabricksFunctions.getDataQualityMonitors(GetDataQualityMonitorsArgs.builder()
            .build());

    }
}

    variables:
  all:
    fn::invoke:
      function: databricks:getDataQualityMonitors
      arguments: {}

    Using getDataQualityMonitors

    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 getDataQualityMonitors(args: GetDataQualityMonitorsArgs, opts?: InvokeOptions): Promise<GetDataQualityMonitorsResult>
function getDataQualityMonitorsOutput(args: GetDataQualityMonitorsOutputArgs, opts?: InvokeOptions): Output<GetDataQualityMonitorsResult>
    def get_data_quality_monitors(page_size: Optional[int] = None,
                              opts: Optional[InvokeOptions] = None) -> GetDataQualityMonitorsResult
def get_data_quality_monitors_output(page_size: Optional[pulumi.Input[int]] = None,
                              opts: Optional[InvokeOptions] = None) -> Output[GetDataQualityMonitorsResult]
    func GetDataQualityMonitors(ctx *Context, args *GetDataQualityMonitorsArgs, opts ...InvokeOption) (*GetDataQualityMonitorsResult, error)
func GetDataQualityMonitorsOutput(ctx *Context, args *GetDataQualityMonitorsOutputArgs, opts ...InvokeOption) GetDataQualityMonitorsResultOutput

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

    public static class GetDataQualityMonitors 
{
    public static Task<GetDataQualityMonitorsResult> InvokeAsync(GetDataQualityMonitorsArgs args, InvokeOptions? opts = null)
    public static Output<GetDataQualityMonitorsResult> Invoke(GetDataQualityMonitorsInvokeArgs args, InvokeOptions? opts = null)
}
    public static CompletableFuture<GetDataQualityMonitorsResult> getDataQualityMonitors(GetDataQualityMonitorsArgs args, InvokeOptions options)
public static Output<GetDataQualityMonitorsResult> getDataQualityMonitors(GetDataQualityMonitorsArgs args, InvokeOptions options)

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

    The following arguments are supported:

    pageSize Integer
    pageSize number
    pageSize Number

    getDataQualityMonitors Result

    The following output properties are available:

    Id string
    The provider-assigned unique ID for this managed resource.
    Monitors List<GetDataQualityMonitorsMonitor>
    PageSize int
    Id string
    The provider-assigned unique ID for this managed resource.
    Monitors []GetDataQualityMonitorsMonitor
    PageSize int
    id String
    The provider-assigned unique ID for this managed resource.
    monitors List<GetDataQualityMonitorsMonitor>
    pageSize Integer
    id string
    The provider-assigned unique ID for this managed resource.
    monitors GetDataQualityMonitorsMonitor[]
    pageSize number
    id str
    The provider-assigned unique ID for this managed resource.
    monitors Sequence[GetDataQualityMonitorsMonitor]
    page_size int
    id String
    The provider-assigned unique ID for this managed resource.
    monitors List<Property Map>
    pageSize Number

    Supporting Types

    GetDataQualityMonitorsMonitor

    AnomalyDetectionConfig GetDataQualityMonitorsMonitorAnomalyDetectionConfig
    (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
    DataProfilingConfig GetDataQualityMonitorsMonitorDataProfilingConfig
    (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
    ObjectId string
    (string) - The UUID of the request object. It is schema_id for schema, and table_id for table.
    ObjectType string
    (string) - The type of the monitored object. Can be one of the following: schema or table
    AnomalyDetectionConfig GetDataQualityMonitorsMonitorAnomalyDetectionConfig
    (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
    DataProfilingConfig GetDataQualityMonitorsMonitorDataProfilingConfig
    (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
    ObjectId string
    (string) - The UUID of the request object. It is schema_id for schema, and table_id for table.
    ObjectType string
    (string) - The type of the monitored object. Can be one of the following: schema or table
    anomalyDetectionConfig GetDataQualityMonitorsMonitorAnomalyDetectionConfig
    (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
    dataProfilingConfig GetDataQualityMonitorsMonitorDataProfilingConfig
    (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
    objectId String
    (string) - The UUID of the request object. It is schema_id for schema, and table_id for table.
    objectType String
    (string) - The type of the monitored object. Can be one of the following: schema or table
    anomalyDetectionConfig GetDataQualityMonitorsMonitorAnomalyDetectionConfig
    (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
    dataProfilingConfig GetDataQualityMonitorsMonitorDataProfilingConfig
    (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
    objectId string
    (string) - The UUID of the request object. It is schema_id for schema, and table_id for table.
    objectType string
    (string) - The type of the monitored object. Can be one of the following: schema or table
    anomaly_detection_config GetDataQualityMonitorsMonitorAnomalyDetectionConfig
    (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
    data_profiling_config GetDataQualityMonitorsMonitorDataProfilingConfig
    (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
    object_id str
    (string) - The UUID of the request object. It is schema_id for schema, and table_id for table.
    object_type str
    (string) - The type of the monitored object. Can be one of the following: schema or table
    anomalyDetectionConfig Property Map
    (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
    dataProfilingConfig Property Map
    (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
    objectId String
    (string) - The UUID of the request object. It is schema_id for schema, and table_id for table.
    objectType String
    (string) - The type of the monitored object. Can be one of the following: schema or table

    GetDataQualityMonitorsMonitorDataProfilingConfig

    DashboardId string
    (string) - Id of dashboard that visualizes the computed metrics. This can be empty if the monitor is in PENDING state
    DriftMetricsTableName string
    (string) - Table that stores drift metrics data. Format: catalog.schema.table_name
    EffectiveWarehouseId string
    (string) - The warehouse for dashboard creation
    LatestMonitorFailureMessage string
    (string) - The latest error message for a monitor failure
    MonitorVersion int
    (integer) - Represents the current monitor configuration version in use. The version will be represented in a numeric fashion (1,2,3...). The field has flexibility to take on negative values, which can indicate corrupted monitor_version numbers
    MonitoredTableName string
    (string) - Unity Catalog table to monitor. Format: catalog.schema.table_name
    OutputSchemaId string
    (string) - ID of the schema where output tables are created
    ProfileMetricsTableName string
    (string) - Table that stores profile metrics data. Format: catalog.schema.table_name
    Status string
    (string) - The data profiling monitor status. Possible values are: DATA_PROFILING_STATUS_ACTIVE, DATA_PROFILING_STATUS_DELETE_PENDING, DATA_PROFILING_STATUS_ERROR, DATA_PROFILING_STATUS_FAILED, DATA_PROFILING_STATUS_PENDING
    AssetsDir string
    (string) - Field for specifying the absolute path to a custom directory to store data-monitoring assets. Normally prepopulated to a default user location via UI and Python APIs
    BaselineTableName string
    (string) - Baseline table name. Baseline data is used to compute drift from the data in the monitored table_name. The baseline table and the monitored table shall have the same schema
    CustomMetrics List<GetDataQualityMonitorsMonitorDataProfilingConfigCustomMetric>
    (list of DataProfilingCustomMetric) - Custom metrics
    InferenceLog GetDataQualityMonitorsMonitorDataProfilingConfigInferenceLog
    (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
    NotificationSettings GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettings
    (NotificationSettings) - Field for specifying notification settings
    Schedule GetDataQualityMonitorsMonitorDataProfilingConfigSchedule
    (CronSchedule) - The cron schedule
    SkipBuiltinDashboard bool
    (boolean) - Whether to skip creating a default dashboard summarizing data quality metrics
    SlicingExprs List<string>
    (list of string) - List of column expressions to slice data with for targeted analysis. The data is grouped by each expression independently, resulting in a separate slice for each predicate and its complements. For example slicing_exprs=[“col_1”, “col_2 > 10”] will generate the following slices: two slices for col_2 </span>> 10 (True and False), and one slice per unique value in col1. For high-cardinality columns, only the top 100 unique values by frequency will generate slices
    Snapshot GetDataQualityMonitorsMonitorDataProfilingConfigSnapshot
    (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
    TimeSeries GetDataQualityMonitorsMonitorDataProfilingConfigTimeSeries
    (TimeSeriesConfig) - Analysis Configuration for monitoring time series tables
    WarehouseId string
    (string) - Optional argument to specify the warehouse for dashboard creation. If not specified, the first running warehouse will be used
    DashboardId string
    (string) - Id of dashboard that visualizes the computed metrics. This can be empty if the monitor is in PENDING state
    DriftMetricsTableName string
    (string) - Table that stores drift metrics data. Format: catalog.schema.table_name
    EffectiveWarehouseId string
    (string) - The warehouse for dashboard creation
    LatestMonitorFailureMessage string
    (string) - The latest error message for a monitor failure
    MonitorVersion int
    (integer) - Represents the current monitor configuration version in use. The version will be represented in a numeric fashion (1,2,3...). The field has flexibility to take on negative values, which can indicate corrupted monitor_version numbers
    MonitoredTableName string
    (string) - Unity Catalog table to monitor. Format: catalog.schema.table_name
    OutputSchemaId string
    (string) - ID of the schema where output tables are created
    ProfileMetricsTableName string
    (string) - Table that stores profile metrics data. Format: catalog.schema.table_name
    Status string
    (string) - The data profiling monitor status. Possible values are: DATA_PROFILING_STATUS_ACTIVE, DATA_PROFILING_STATUS_DELETE_PENDING, DATA_PROFILING_STATUS_ERROR, DATA_PROFILING_STATUS_FAILED, DATA_PROFILING_STATUS_PENDING
    AssetsDir string
    (string) - Field for specifying the absolute path to a custom directory to store data-monitoring assets. Normally prepopulated to a default user location via UI and Python APIs
    BaselineTableName string
    (string) - Baseline table name. Baseline data is used to compute drift from the data in the monitored table_name. The baseline table and the monitored table shall have the same schema
    CustomMetrics []GetDataQualityMonitorsMonitorDataProfilingConfigCustomMetric
    (list of DataProfilingCustomMetric) - Custom metrics
    InferenceLog GetDataQualityMonitorsMonitorDataProfilingConfigInferenceLog
    (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
    NotificationSettings GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettings
    (NotificationSettings) - Field for specifying notification settings
    Schedule GetDataQualityMonitorsMonitorDataProfilingConfigSchedule
    (CronSchedule) - The cron schedule
    SkipBuiltinDashboard bool
    (boolean) - Whether to skip creating a default dashboard summarizing data quality metrics
    SlicingExprs []string
    (list of string) - List of column expressions to slice data with for targeted analysis. The data is grouped by each expression independently, resulting in a separate slice for each predicate and its complements. For example slicing_exprs=[“col_1”, “col_2 > 10”] will generate the following slices: two slices for col_2 </span>> 10 (True and False), and one slice per unique value in col1. For high-cardinality columns, only the top 100 unique values by frequency will generate slices
    Snapshot GetDataQualityMonitorsMonitorDataProfilingConfigSnapshot
    (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
    TimeSeries GetDataQualityMonitorsMonitorDataProfilingConfigTimeSeries
    (TimeSeriesConfig) - Analysis Configuration for monitoring time series tables
    WarehouseId string
    (string) - Optional argument to specify the warehouse for dashboard creation. If not specified, the first running warehouse will be used
    dashboardId String
    (string) - Id of dashboard that visualizes the computed metrics. This can be empty if the monitor is in PENDING state
    driftMetricsTableName String
    (string) - Table that stores drift metrics data. Format: catalog.schema.table_name
    effectiveWarehouseId String
    (string) - The warehouse for dashboard creation
    latestMonitorFailureMessage String
    (string) - The latest error message for a monitor failure
    monitorVersion Integer
    (integer) - Represents the current monitor configuration version in use. The version will be represented in a numeric fashion (1,2,3...). The field has flexibility to take on negative values, which can indicate corrupted monitor_version numbers
    monitoredTableName String
    (string) - Unity Catalog table to monitor. Format: catalog.schema.table_name
    outputSchemaId String
    (string) - ID of the schema where output tables are created
    profileMetricsTableName String
    (string) - Table that stores profile metrics data. Format: catalog.schema.table_name
    status String
    (string) - The data profiling monitor status. Possible values are: DATA_PROFILING_STATUS_ACTIVE, DATA_PROFILING_STATUS_DELETE_PENDING, DATA_PROFILING_STATUS_ERROR, DATA_PROFILING_STATUS_FAILED, DATA_PROFILING_STATUS_PENDING
    assetsDir String
    (string) - Field for specifying the absolute path to a custom directory to store data-monitoring assets. Normally prepopulated to a default user location via UI and Python APIs
    baselineTableName String
    (string) - Baseline table name. Baseline data is used to compute drift from the data in the monitored table_name. The baseline table and the monitored table shall have the same schema
    customMetrics List<GetDataQualityMonitorsMonitorDataProfilingConfigCustomMetric>
    (list of DataProfilingCustomMetric) - Custom metrics
    inferenceLog GetDataQualityMonitorsMonitorDataProfilingConfigInferenceLog
    (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
    notificationSettings GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettings
    (NotificationSettings) - Field for specifying notification settings
    schedule GetDataQualityMonitorsMonitorDataProfilingConfigSchedule
    (CronSchedule) - The cron schedule
    skipBuiltinDashboard Boolean
    (boolean) - Whether to skip creating a default dashboard summarizing data quality metrics
    slicingExprs List<String>
    (list of string) - List of column expressions to slice data with for targeted analysis. The data is grouped by each expression independently, resulting in a separate slice for each predicate and its complements. For example slicing_exprs=[“col_1”, “col_2 > 10”] will generate the following slices: two slices for col_2 </span>> 10 (True and False), and one slice per unique value in col1. For high-cardinality columns, only the top 100 unique values by frequency will generate slices
    snapshot GetDataQualityMonitorsMonitorDataProfilingConfigSnapshot
    (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
    timeSeries GetDataQualityMonitorsMonitorDataProfilingConfigTimeSeries
    (TimeSeriesConfig) - Analysis Configuration for monitoring time series tables
    warehouseId String
    (string) - Optional argument to specify the warehouse for dashboard creation. If not specified, the first running warehouse will be used
    dashboardId string
    (string) - Id of dashboard that visualizes the computed metrics. This can be empty if the monitor is in PENDING state
    driftMetricsTableName string
    (string) - Table that stores drift metrics data. Format: catalog.schema.table_name
    effectiveWarehouseId string
    (string) - The warehouse for dashboard creation
    latestMonitorFailureMessage string
    (string) - The latest error message for a monitor failure
    monitorVersion number
    (integer) - Represents the current monitor configuration version in use. The version will be represented in a numeric fashion (1,2,3...). The field has flexibility to take on negative values, which can indicate corrupted monitor_version numbers
    monitoredTableName string
    (string) - Unity Catalog table to monitor. Format: catalog.schema.table_name
    outputSchemaId string
    (string) - ID of the schema where output tables are created
    profileMetricsTableName string
    (string) - Table that stores profile metrics data. Format: catalog.schema.table_name
    status string
    (string) - The data profiling monitor status. Possible values are: DATA_PROFILING_STATUS_ACTIVE, DATA_PROFILING_STATUS_DELETE_PENDING, DATA_PROFILING_STATUS_ERROR, DATA_PROFILING_STATUS_FAILED, DATA_PROFILING_STATUS_PENDING
    assetsDir string
    (string) - Field for specifying the absolute path to a custom directory to store data-monitoring assets. Normally prepopulated to a default user location via UI and Python APIs
    baselineTableName string
    (string) - Baseline table name. Baseline data is used to compute drift from the data in the monitored table_name. The baseline table and the monitored table shall have the same schema
    customMetrics GetDataQualityMonitorsMonitorDataProfilingConfigCustomMetric[]
    (list of DataProfilingCustomMetric) - Custom metrics
    inferenceLog GetDataQualityMonitorsMonitorDataProfilingConfigInferenceLog
    (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
    notificationSettings GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettings
    (NotificationSettings) - Field for specifying notification settings
    schedule GetDataQualityMonitorsMonitorDataProfilingConfigSchedule
    (CronSchedule) - The cron schedule
    skipBuiltinDashboard boolean
    (boolean) - Whether to skip creating a default dashboard summarizing data quality metrics
    slicingExprs string[]
    (list of string) - List of column expressions to slice data with for targeted analysis. The data is grouped by each expression independently, resulting in a separate slice for each predicate and its complements. For example slicing_exprs=[“col_1”, “col_2 > 10”] will generate the following slices: two slices for col_2 </span>> 10 (True and False), and one slice per unique value in col1. For high-cardinality columns, only the top 100 unique values by frequency will generate slices
    snapshot GetDataQualityMonitorsMonitorDataProfilingConfigSnapshot
    (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
    timeSeries GetDataQualityMonitorsMonitorDataProfilingConfigTimeSeries
    (TimeSeriesConfig) - Analysis Configuration for monitoring time series tables
    warehouseId string
    (string) - Optional argument to specify the warehouse for dashboard creation. If not specified, the first running warehouse will be used
    dashboard_id str
    (string) - Id of dashboard that visualizes the computed metrics. This can be empty if the monitor is in PENDING state
    drift_metrics_table_name str
    (string) - Table that stores drift metrics data. Format: catalog.schema.table_name
    effective_warehouse_id str
    (string) - The warehouse for dashboard creation
    latest_monitor_failure_message str
    (string) - The latest error message for a monitor failure
    monitor_version int
    (integer) - Represents the current monitor configuration version in use. The version will be represented in a numeric fashion (1,2,3...). The field has flexibility to take on negative values, which can indicate corrupted monitor_version numbers
    monitored_table_name str
    (string) - Unity Catalog table to monitor. Format: catalog.schema.table_name
    output_schema_id str
    (string) - ID of the schema where output tables are created
    profile_metrics_table_name str
    (string) - Table that stores profile metrics data. Format: catalog.schema.table_name
    status str
    (string) - The data profiling monitor status. Possible values are: DATA_PROFILING_STATUS_ACTIVE, DATA_PROFILING_STATUS_DELETE_PENDING, DATA_PROFILING_STATUS_ERROR, DATA_PROFILING_STATUS_FAILED, DATA_PROFILING_STATUS_PENDING
    assets_dir str
    (string) - Field for specifying the absolute path to a custom directory to store data-monitoring assets. Normally prepopulated to a default user location via UI and Python APIs
    baseline_table_name str
    (string) - Baseline table name. Baseline data is used to compute drift from the data in the monitored table_name. The baseline table and the monitored table shall have the same schema
    custom_metrics Sequence[GetDataQualityMonitorsMonitorDataProfilingConfigCustomMetric]
    (list of DataProfilingCustomMetric) - Custom metrics
    inference_log GetDataQualityMonitorsMonitorDataProfilingConfigInferenceLog
    (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
    notification_settings GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettings
    (NotificationSettings) - Field for specifying notification settings
    schedule GetDataQualityMonitorsMonitorDataProfilingConfigSchedule
    (CronSchedule) - The cron schedule
    skip_builtin_dashboard bool
    (boolean) - Whether to skip creating a default dashboard summarizing data quality metrics
    slicing_exprs Sequence[str]
    (list of string) - List of column expressions to slice data with for targeted analysis. The data is grouped by each expression independently, resulting in a separate slice for each predicate and its complements. For example slicing_exprs=[“col_1”, “col_2 > 10”] will generate the following slices: two slices for col_2 </span>> 10 (True and False), and one slice per unique value in col1. For high-cardinality columns, only the top 100 unique values by frequency will generate slices
    snapshot GetDataQualityMonitorsMonitorDataProfilingConfigSnapshot
    (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
    time_series GetDataQualityMonitorsMonitorDataProfilingConfigTimeSeries
    (TimeSeriesConfig) - Analysis Configuration for monitoring time series tables
    warehouse_id str
    (string) - Optional argument to specify the warehouse for dashboard creation. If not specified, the first running warehouse will be used
    dashboardId String
    (string) - Id of dashboard that visualizes the computed metrics. This can be empty if the monitor is in PENDING state
    driftMetricsTableName String
    (string) - Table that stores drift metrics data. Format: catalog.schema.table_name
    effectiveWarehouseId String
    (string) - The warehouse for dashboard creation
    latestMonitorFailureMessage String
    (string) - The latest error message for a monitor failure
    monitorVersion Number
    (integer) - Represents the current monitor configuration version in use. The version will be represented in a numeric fashion (1,2,3...). The field has flexibility to take on negative values, which can indicate corrupted monitor_version numbers
    monitoredTableName String
    (string) - Unity Catalog table to monitor. Format: catalog.schema.table_name
    outputSchemaId String
    (string) - ID of the schema where output tables are created
    profileMetricsTableName String
    (string) - Table that stores profile metrics data. Format: catalog.schema.table_name
    status String
    (string) - The data profiling monitor status. Possible values are: DATA_PROFILING_STATUS_ACTIVE, DATA_PROFILING_STATUS_DELETE_PENDING, DATA_PROFILING_STATUS_ERROR, DATA_PROFILING_STATUS_FAILED, DATA_PROFILING_STATUS_PENDING
    assetsDir String
    (string) - Field for specifying the absolute path to a custom directory to store data-monitoring assets. Normally prepopulated to a default user location via UI and Python APIs
    baselineTableName String
    (string) - Baseline table name. Baseline data is used to compute drift from the data in the monitored table_name. The baseline table and the monitored table shall have the same schema
    customMetrics List<Property Map>
    (list of DataProfilingCustomMetric) - Custom metrics
    inferenceLog Property Map
    (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
    notificationSettings Property Map
    (NotificationSettings) - Field for specifying notification settings
    schedule Property Map
    (CronSchedule) - The cron schedule
    skipBuiltinDashboard Boolean
    (boolean) - Whether to skip creating a default dashboard summarizing data quality metrics
    slicingExprs List<String>
    (list of string) - List of column expressions to slice data with for targeted analysis. The data is grouped by each expression independently, resulting in a separate slice for each predicate and its complements. For example slicing_exprs=[“col_1”, “col_2 > 10”] will generate the following slices: two slices for col_2 </span>> 10 (True and False), and one slice per unique value in col1. For high-cardinality columns, only the top 100 unique values by frequency will generate slices
    snapshot Property Map
    (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
    timeSeries Property Map
    (TimeSeriesConfig) - Analysis Configuration for monitoring time series tables
    warehouseId String
    (string) - Optional argument to specify the warehouse for dashboard creation. If not specified, the first running warehouse will be used

    GetDataQualityMonitorsMonitorDataProfilingConfigCustomMetric

    Definition string
    (string) - Jinja template for a SQL expression that specifies how to compute the metric. See create metric definition
    InputColumns List<string>
    (list of string) - A list of column names in the input table the metric should be computed for. Can use ":table" to indicate that the metric needs information from multiple columns
    Name string
    (string) - Name of the metric in the output tables
    OutputDataType string
    (string) - The output type of the custom metric
    Type string
    (string) - The type of the custom metric. Possible values are: DATA_PROFILING_CUSTOM_METRIC_TYPE_AGGREGATE, DATA_PROFILING_CUSTOM_METRIC_TYPE_DERIVED, DATA_PROFILING_CUSTOM_METRIC_TYPE_DRIFT
    Definition string
    (string) - Jinja template for a SQL expression that specifies how to compute the metric. See create metric definition
    InputColumns []string
    (list of string) - A list of column names in the input table the metric should be computed for. Can use ":table" to indicate that the metric needs information from multiple columns
    Name string
    (string) - Name of the metric in the output tables
    OutputDataType string
    (string) - The output type of the custom metric
    Type string
    (string) - The type of the custom metric. Possible values are: DATA_PROFILING_CUSTOM_METRIC_TYPE_AGGREGATE, DATA_PROFILING_CUSTOM_METRIC_TYPE_DERIVED, DATA_PROFILING_CUSTOM_METRIC_TYPE_DRIFT
    definition String
    (string) - Jinja template for a SQL expression that specifies how to compute the metric. See create metric definition
    inputColumns List<String>
    (list of string) - A list of column names in the input table the metric should be computed for. Can use ":table" to indicate that the metric needs information from multiple columns
    name String
    (string) - Name of the metric in the output tables
    outputDataType String
    (string) - The output type of the custom metric
    type String
    (string) - The type of the custom metric. Possible values are: DATA_PROFILING_CUSTOM_METRIC_TYPE_AGGREGATE, DATA_PROFILING_CUSTOM_METRIC_TYPE_DERIVED, DATA_PROFILING_CUSTOM_METRIC_TYPE_DRIFT
    definition string
    (string) - Jinja template for a SQL expression that specifies how to compute the metric. See create metric definition
    inputColumns string[]
    (list of string) - A list of column names in the input table the metric should be computed for. Can use ":table" to indicate that the metric needs information from multiple columns
    name string
    (string) - Name of the metric in the output tables
    outputDataType string
    (string) - The output type of the custom metric
    type string
    (string) - The type of the custom metric. Possible values are: DATA_PROFILING_CUSTOM_METRIC_TYPE_AGGREGATE, DATA_PROFILING_CUSTOM_METRIC_TYPE_DERIVED, DATA_PROFILING_CUSTOM_METRIC_TYPE_DRIFT
    definition str
    (string) - Jinja template for a SQL expression that specifies how to compute the metric. See create metric definition
    input_columns Sequence[str]
    (list of string) - A list of column names in the input table the metric should be computed for. Can use ":table" to indicate that the metric needs information from multiple columns
    name str
    (string) - Name of the metric in the output tables
    output_data_type str
    (string) - The output type of the custom metric
    type str
    (string) - The type of the custom metric. Possible values are: DATA_PROFILING_CUSTOM_METRIC_TYPE_AGGREGATE, DATA_PROFILING_CUSTOM_METRIC_TYPE_DERIVED, DATA_PROFILING_CUSTOM_METRIC_TYPE_DRIFT
    definition String
    (string) - Jinja template for a SQL expression that specifies how to compute the metric. See create metric definition
    inputColumns List<String>
    (list of string) - A list of column names in the input table the metric should be computed for. Can use ":table" to indicate that the metric needs information from multiple columns
    name String
    (string) - Name of the metric in the output tables
    outputDataType String
    (string) - The output type of the custom metric
    type String
    (string) - The type of the custom metric. Possible values are: DATA_PROFILING_CUSTOM_METRIC_TYPE_AGGREGATE, DATA_PROFILING_CUSTOM_METRIC_TYPE_DERIVED, DATA_PROFILING_CUSTOM_METRIC_TYPE_DRIFT

    GetDataQualityMonitorsMonitorDataProfilingConfigInferenceLog

    Granularities List<string>
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    ModelIdColumn string
    (string) - Column for the model identifier
    PredictionColumn string
    (string) - Column for the prediction
    ProblemType string
    (string) - Problem type the model aims to solve. Possible values are: INFERENCE_PROBLEM_TYPE_CLASSIFICATION, INFERENCE_PROBLEM_TYPE_REGRESSION
    TimestampColumn string
    (string) - Column for the timestamp
    LabelColumn string
    (string) - Column for the label
    Granularities []string
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    ModelIdColumn string
    (string) - Column for the model identifier
    PredictionColumn string
    (string) - Column for the prediction
    ProblemType string
    (string) - Problem type the model aims to solve. Possible values are: INFERENCE_PROBLEM_TYPE_CLASSIFICATION, INFERENCE_PROBLEM_TYPE_REGRESSION
    TimestampColumn string
    (string) - Column for the timestamp
    LabelColumn string
    (string) - Column for the label
    granularities List<String>
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    modelIdColumn String
    (string) - Column for the model identifier
    predictionColumn String
    (string) - Column for the prediction
    problemType String
    (string) - Problem type the model aims to solve. Possible values are: INFERENCE_PROBLEM_TYPE_CLASSIFICATION, INFERENCE_PROBLEM_TYPE_REGRESSION
    timestampColumn String
    (string) - Column for the timestamp
    labelColumn String
    (string) - Column for the label
    granularities string[]
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    modelIdColumn string
    (string) - Column for the model identifier
    predictionColumn string
    (string) - Column for the prediction
    problemType string
    (string) - Problem type the model aims to solve. Possible values are: INFERENCE_PROBLEM_TYPE_CLASSIFICATION, INFERENCE_PROBLEM_TYPE_REGRESSION
    timestampColumn string
    (string) - Column for the timestamp
    labelColumn string
    (string) - Column for the label
    granularities Sequence[str]
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    model_id_column str
    (string) - Column for the model identifier
    prediction_column str
    (string) - Column for the prediction
    problem_type str
    (string) - Problem type the model aims to solve. Possible values are: INFERENCE_PROBLEM_TYPE_CLASSIFICATION, INFERENCE_PROBLEM_TYPE_REGRESSION
    timestamp_column str
    (string) - Column for the timestamp
    label_column str
    (string) - Column for the label
    granularities List<String>
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    modelIdColumn String
    (string) - Column for the model identifier
    predictionColumn String
    (string) - Column for the prediction
    problemType String
    (string) - Problem type the model aims to solve. Possible values are: INFERENCE_PROBLEM_TYPE_CLASSIFICATION, INFERENCE_PROBLEM_TYPE_REGRESSION
    timestampColumn String
    (string) - Column for the timestamp
    labelColumn String
    (string) - Column for the label

    GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettings

    OnFailure GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettingsOnFailure
    (NotificationDestination) - Destinations to send notifications on failure/timeout
    OnFailure GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettingsOnFailure
    (NotificationDestination) - Destinations to send notifications on failure/timeout
    onFailure GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettingsOnFailure
    (NotificationDestination) - Destinations to send notifications on failure/timeout
    onFailure GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettingsOnFailure
    (NotificationDestination) - Destinations to send notifications on failure/timeout
    on_failure GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettingsOnFailure
    (NotificationDestination) - Destinations to send notifications on failure/timeout
    onFailure Property Map
    (NotificationDestination) - Destinations to send notifications on failure/timeout

    GetDataQualityMonitorsMonitorDataProfilingConfigNotificationSettingsOnFailure

    EmailAddresses List<string>
    (list of string) - The list of email addresses to send the notification to. A maximum of 5 email addresses is supported
    EmailAddresses []string
    (list of string) - The list of email addresses to send the notification to. A maximum of 5 email addresses is supported
    emailAddresses List<String>
    (list of string) - The list of email addresses to send the notification to. A maximum of 5 email addresses is supported
    emailAddresses string[]
    (list of string) - The list of email addresses to send the notification to. A maximum of 5 email addresses is supported
    email_addresses Sequence[str]
    (list of string) - The list of email addresses to send the notification to. A maximum of 5 email addresses is supported
    emailAddresses List<String>
    (list of string) - The list of email addresses to send the notification to. A maximum of 5 email addresses is supported

    GetDataQualityMonitorsMonitorDataProfilingConfigSchedule

    PauseStatus string
    (string) - Read only field that indicates whether the schedule is paused or not. Possible values are: CRON_SCHEDULE_PAUSE_STATUS_PAUSED, CRON_SCHEDULE_PAUSE_STATUS_UNPAUSED
    QuartzCronExpression string
    (string) - The expression that determines when to run the monitor. See examples
    TimezoneId string
    (string) - A Java timezone id. The schedule for a job will be resolved with respect to this timezone. See Java TimeZone <http://docs.oracle.com/javase/7/docs/api/java/util/TimeZone.html>_ for details. The timezone id (e.g., America/Los_Angeles) in which to evaluate the quartz expression
    PauseStatus string
    (string) - Read only field that indicates whether the schedule is paused or not. Possible values are: CRON_SCHEDULE_PAUSE_STATUS_PAUSED, CRON_SCHEDULE_PAUSE_STATUS_UNPAUSED
    QuartzCronExpression string
    (string) - The expression that determines when to run the monitor. See examples
    TimezoneId string
    (string) - A Java timezone id. The schedule for a job will be resolved with respect to this timezone. See Java TimeZone <http://docs.oracle.com/javase/7/docs/api/java/util/TimeZone.html>_ for details. The timezone id (e.g., America/Los_Angeles) in which to evaluate the quartz expression
    pauseStatus String
    (string) - Read only field that indicates whether the schedule is paused or not. Possible values are: CRON_SCHEDULE_PAUSE_STATUS_PAUSED, CRON_SCHEDULE_PAUSE_STATUS_UNPAUSED
    quartzCronExpression String
    (string) - The expression that determines when to run the monitor. See examples
    timezoneId String
    (string) - A Java timezone id. The schedule for a job will be resolved with respect to this timezone. See Java TimeZone <http://docs.oracle.com/javase/7/docs/api/java/util/TimeZone.html>_ for details. The timezone id (e.g., America/Los_Angeles) in which to evaluate the quartz expression
    pauseStatus string
    (string) - Read only field that indicates whether the schedule is paused or not. Possible values are: CRON_SCHEDULE_PAUSE_STATUS_PAUSED, CRON_SCHEDULE_PAUSE_STATUS_UNPAUSED
    quartzCronExpression string
    (string) - The expression that determines when to run the monitor. See examples
    timezoneId string
    (string) - A Java timezone id. The schedule for a job will be resolved with respect to this timezone. See Java TimeZone <http://docs.oracle.com/javase/7/docs/api/java/util/TimeZone.html>_ for details. The timezone id (e.g., America/Los_Angeles) in which to evaluate the quartz expression
    pause_status str
    (string) - Read only field that indicates whether the schedule is paused or not. Possible values are: CRON_SCHEDULE_PAUSE_STATUS_PAUSED, CRON_SCHEDULE_PAUSE_STATUS_UNPAUSED
    quartz_cron_expression str
    (string) - The expression that determines when to run the monitor. See examples
    timezone_id str
    (string) - A Java timezone id. The schedule for a job will be resolved with respect to this timezone. See Java TimeZone <http://docs.oracle.com/javase/7/docs/api/java/util/TimeZone.html>_ for details. The timezone id (e.g., America/Los_Angeles) in which to evaluate the quartz expression
    pauseStatus String
    (string) - Read only field that indicates whether the schedule is paused or not. Possible values are: CRON_SCHEDULE_PAUSE_STATUS_PAUSED, CRON_SCHEDULE_PAUSE_STATUS_UNPAUSED
    quartzCronExpression String
    (string) - The expression that determines when to run the monitor. See examples
    timezoneId String
    (string) - A Java timezone id. The schedule for a job will be resolved with respect to this timezone. See Java TimeZone <http://docs.oracle.com/javase/7/docs/api/java/util/TimeZone.html>_ for details. The timezone id (e.g., America/Los_Angeles) in which to evaluate the quartz expression

    GetDataQualityMonitorsMonitorDataProfilingConfigTimeSeries

    Granularities List<string>
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    TimestampColumn string
    (string) - Column for the timestamp
    Granularities []string
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    TimestampColumn string
    (string) - Column for the timestamp
    granularities List<String>
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    timestampColumn String
    (string) - Column for the timestamp
    granularities string[]
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    timestampColumn string
    (string) - Column for the timestamp
    granularities Sequence[str]
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    timestamp_column str
    (string) - Column for the timestamp
    granularities List<String>
    (list of string) - List of granularities to use when aggregating data into time windows based on their timestamp
    timestampColumn String
    (string) - Column for the timestamp

    Package Details

    Repository
    databricks pulumi/pulumi-databricks
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the databricks Terraform Provider.
    databricks logo
    Databricks v1.78.0 published on Friday, Nov 7, 2025 by Pulumi
    Schema (JSON)
    pulumi/pulumi-databricks
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