Databricks v1.78.0, Nov 7 25

Databricks v1.78.0 published on Friday, Nov 7, 2025 by Pulumi

pulumi/pulumi-databricks

databricks.getDataQualityMonitor

Databricks v1.78.0 published on Friday, Nov 7, 2025 by Pulumi

Schema (JSON)

pulumi/pulumi-databricks

be an owner of the table’s parent catalog
have USE_CATALOG on the table’s parent catalog and be an owner of the table’s parent schema.
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 data quality monitor by Unity Catalog object type (currently supports schema and table) and object id:

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

const _this = databricks.getSchema({
    name: "my_catalog.my_schema",
});
const thisGetDataQualityMonitor = _this.then(_this => databricks.getDataQualityMonitor({
    objectType: "schema",
    objectId: _this.schemaInfo?.schemaId,
}));

import pulumi
import pulumi_databricks as databricks

this = databricks.get_schema(name="my_catalog.my_schema")
this_get_data_quality_monitor = databricks.get_data_quality_monitor(object_type="schema",
    object_id=this.schema_info.schema_id)

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 {
		this, err := databricks.LookupSchema(ctx, &databricks.LookupSchemaArgs{
			Name: "my_catalog.my_schema",
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.LookupDataQualityMonitor(ctx, &databricks.LookupDataQualityMonitorArgs{
			ObjectType: "schema",
			ObjectId:   this.SchemaInfo.SchemaId,
		}, 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 @this = Databricks.GetSchema.Invoke(new()
    {
        Name = "my_catalog.my_schema",
    });

    var thisGetDataQualityMonitor = Databricks.GetDataQualityMonitor.Invoke(new()
    {
        ObjectType = "schema",
        ObjectId = @this.Apply(getSchemaResult => getSchemaResult.SchemaInfo?.SchemaId),
    });

});

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.GetSchemaArgs;
import com.pulumi.databricks.inputs.GetDataQualityMonitorArgs;
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 this = DatabricksFunctions.getSchema(GetSchemaArgs.builder()
            .name("my_catalog.my_schema")
            .build());

        final var thisGetDataQualityMonitor = DatabricksFunctions.getDataQualityMonitor(GetDataQualityMonitorArgs.builder()
            .objectType("schema")
            .objectId(this_.schemaInfo().schemaId())
            .build());

    }
}

variables:
  this:
    fn::invoke:
      function: databricks:getSchema
      arguments:
        name: my_catalog.my_schema
  thisGetDataQualityMonitor:
    fn::invoke:
      function: databricks:getDataQualityMonitor
      arguments:
        objectType: schema
        objectId: ${this.schemaInfo.schemaId}

Using getDataQualityMonitor

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 getDataQualityMonitor(args: GetDataQualityMonitorArgs, opts?: InvokeOptions): Promise<GetDataQualityMonitorResult>
function getDataQualityMonitorOutput(args: GetDataQualityMonitorOutputArgs, opts?: InvokeOptions): Output<GetDataQualityMonitorResult>

def get_data_quality_monitor(object_id: Optional[str] = None,
                             object_type: Optional[str] = None,
                             opts: Optional[InvokeOptions] = None) -> GetDataQualityMonitorResult
def get_data_quality_monitor_output(object_id: Optional[pulumi.Input[str]] = None,
                             object_type: Optional[pulumi.Input[str]] = None,
                             opts: Optional[InvokeOptions] = None) -> Output[GetDataQualityMonitorResult]

func LookupDataQualityMonitor(ctx *Context, args *LookupDataQualityMonitorArgs, opts ...InvokeOption) (*LookupDataQualityMonitorResult, error)
func LookupDataQualityMonitorOutput(ctx *Context, args *LookupDataQualityMonitorOutputArgs, opts ...InvokeOption) LookupDataQualityMonitorResultOutput

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

public static class GetDataQualityMonitor 
{
    public static Task<GetDataQualityMonitorResult> InvokeAsync(GetDataQualityMonitorArgs args, InvokeOptions? opts = null)
    public static Output<GetDataQualityMonitorResult> Invoke(GetDataQualityMonitorInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetDataQualityMonitorResult> getDataQualityMonitor(GetDataQualityMonitorArgs args, InvokeOptions options)
public static Output<GetDataQualityMonitorResult> getDataQualityMonitor(GetDataQualityMonitorArgs args, InvokeOptions options)

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

The following arguments are supported:

ObjectId string

The UUID of the request object. It is schema_id for schema, and table_id for table.

Find the schema_id from either:

The [schema_id](https://docs.databricks.com/api/workspace/schemas/get#schema_id) of the Schemas resource.
In Catalog Explorer > select the schema > go to the Details tab > the Schema ID field.

Find the table_id from either:

The [table_id](https://docs.databricks.com/api/workspace/tables/get#table_id) of the Tables resource.
In Catalog Explorer > select the table > go to the Details tab > the Table ID field

ObjectType string

The type of the monitored object. Can be one of the following: schema or table

ObjectId string

The UUID of the request object. It is schema_id for schema, and table_id for table.

Find the schema_id from either:

The [schema_id](https://docs.databricks.com/api/workspace/schemas/get#schema_id) of the Schemas resource.
In Catalog Explorer > select the schema > go to the Details tab > the Schema ID field.

Find the table_id from either:

The [table_id](https://docs.databricks.com/api/workspace/tables/get#table_id) of the Tables resource.
In Catalog Explorer > select the table > go to the Details tab > the Table ID field

ObjectType string

The type of the monitored object. Can be one of the following: schema or table

objectId String

The UUID of the request object. It is schema_id for schema, and table_id for table.

Find the schema_id from either:

The [schema_id](https://docs.databricks.com/api/workspace/schemas/get#schema_id) of the Schemas resource.
In Catalog Explorer > select the schema > go to the Details tab > the Schema ID field.

Find the table_id from either:

The [table_id](https://docs.databricks.com/api/workspace/tables/get#table_id) of the Tables resource.
In Catalog Explorer > select the table > go to the Details tab > the Table ID field

objectType String

The type of the monitored object. Can be one of the following: schema or table

objectId string

The UUID of the request object. It is schema_id for schema, and table_id for table.

Find the schema_id from either:

The [schema_id](https://docs.databricks.com/api/workspace/schemas/get#schema_id) of the Schemas resource.
In Catalog Explorer > select the schema > go to the Details tab > the Schema ID field.

Find the table_id from either:

The [table_id](https://docs.databricks.com/api/workspace/tables/get#table_id) of the Tables resource.
In Catalog Explorer > select the table > go to the Details tab > the Table ID field

objectType string

The type of the monitored object. Can be one of the following: schema or table

object_id str

The UUID of the request object. It is schema_id for schema, and table_id for table.

Find the schema_id from either:

The [schema_id](https://docs.databricks.com/api/workspace/schemas/get#schema_id) of the Schemas resource.
In Catalog Explorer > select the schema > go to the Details tab > the Schema ID field.

Find the table_id from either:

The [table_id](https://docs.databricks.com/api/workspace/tables/get#table_id) of the Tables resource.
In Catalog Explorer > select the table > go to the Details tab > the Table ID field

object_type str

The type of the monitored object. Can be one of the following: schema or table

objectId String

The UUID of the request object. It is schema_id for schema, and table_id for table.

Find the schema_id from either:

The [schema_id](https://docs.databricks.com/api/workspace/schemas/get#schema_id) of the Schemas resource.
In Catalog Explorer > select the schema > go to the Details tab > the Schema ID field.

Find the table_id from either:

The [table_id](https://docs.databricks.com/api/workspace/tables/get#table_id) of the Tables resource.
In Catalog Explorer > select the table > go to the Details tab > the Table ID field

objectType String

The type of the monitored object. Can be one of the following: schema or table

getDataQualityMonitor Result

The following output properties are available:

AnomalyDetectionConfig GetDataQualityMonitorAnomalyDetectionConfig: (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
DataProfilingConfig GetDataQualityMonitorDataProfilingConfig: (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
Id string: The provider-assigned unique ID for this managed resource.
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 GetDataQualityMonitorAnomalyDetectionConfig: (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
DataProfilingConfig GetDataQualityMonitorDataProfilingConfig: (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
Id string: The provider-assigned unique ID for this managed resource.
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 GetDataQualityMonitorAnomalyDetectionConfig: (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
dataProfilingConfig GetDataQualityMonitorDataProfilingConfig: (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
id String: The provider-assigned unique ID for this managed resource.
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 GetDataQualityMonitorAnomalyDetectionConfig: (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
dataProfilingConfig GetDataQualityMonitorDataProfilingConfig: (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
id string: The provider-assigned unique ID for this managed resource.
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 GetDataQualityMonitorAnomalyDetectionConfig: (AnomalyDetectionConfig) - Anomaly Detection Configuration, applicable to schema object types
data_profiling_config GetDataQualityMonitorDataProfilingConfig: (DataProfilingConfig) - Data Profiling Configuration, applicable to table object types. Exactly one Analysis Configuration must be present
id str: The provider-assigned unique ID for this managed resource.
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
id String: The provider-assigned unique ID for this managed resource.
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

Supporting Types

GetDataQualityMonitorDataProfilingConfig

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<GetDataQualityMonitorDataProfilingConfigCustomMetric>: (list of DataProfilingCustomMetric) - Custom metrics
InferenceLog GetDataQualityMonitorDataProfilingConfigInferenceLog: (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
NotificationSettings GetDataQualityMonitorDataProfilingConfigNotificationSettings: (NotificationSettings) - Field for specifying notification settings
Schedule GetDataQualityMonitorDataProfilingConfigSchedule: (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 > 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 GetDataQualityMonitorDataProfilingConfigSnapshot: (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
TimeSeries GetDataQualityMonitorDataProfilingConfigTimeSeries: (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 []GetDataQualityMonitorDataProfilingConfigCustomMetric: (list of DataProfilingCustomMetric) - Custom metrics
InferenceLog GetDataQualityMonitorDataProfilingConfigInferenceLog: (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
NotificationSettings GetDataQualityMonitorDataProfilingConfigNotificationSettings: (NotificationSettings) - Field for specifying notification settings
Schedule GetDataQualityMonitorDataProfilingConfigSchedule: (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 > 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 GetDataQualityMonitorDataProfilingConfigSnapshot: (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
TimeSeries GetDataQualityMonitorDataProfilingConfigTimeSeries: (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<GetDataQualityMonitorDataProfilingConfigCustomMetric>: (list of DataProfilingCustomMetric) - Custom metrics
inferenceLog GetDataQualityMonitorDataProfilingConfigInferenceLog: (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
notificationSettings GetDataQualityMonitorDataProfilingConfigNotificationSettings: (NotificationSettings) - Field for specifying notification settings
schedule GetDataQualityMonitorDataProfilingConfigSchedule: (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 > 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 GetDataQualityMonitorDataProfilingConfigSnapshot: (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
timeSeries GetDataQualityMonitorDataProfilingConfigTimeSeries: (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 GetDataQualityMonitorDataProfilingConfigCustomMetric[]: (list of DataProfilingCustomMetric) - Custom metrics
inferenceLog GetDataQualityMonitorDataProfilingConfigInferenceLog: (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
notificationSettings GetDataQualityMonitorDataProfilingConfigNotificationSettings: (NotificationSettings) - Field for specifying notification settings
schedule GetDataQualityMonitorDataProfilingConfigSchedule: (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 > 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 GetDataQualityMonitorDataProfilingConfigSnapshot: (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
timeSeries GetDataQualityMonitorDataProfilingConfigTimeSeries: (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[GetDataQualityMonitorDataProfilingConfigCustomMetric]: (list of DataProfilingCustomMetric) - Custom metrics
inference_log GetDataQualityMonitorDataProfilingConfigInferenceLog: (InferenceLogConfig) - Analysis Configuration for monitoring inference log tables
notification_settings GetDataQualityMonitorDataProfilingConfigNotificationSettings: (NotificationSettings) - Field for specifying notification settings
schedule GetDataQualityMonitorDataProfilingConfigSchedule: (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 > 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 GetDataQualityMonitorDataProfilingConfigSnapshot: (SnapshotConfig) - Analysis Configuration for monitoring snapshot tables
time_series GetDataQualityMonitorDataProfilingConfigTimeSeries: (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 > 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

GetDataQualityMonitorDataProfilingConfigCustomMetric

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

GetDataQualityMonitorDataProfilingConfigInferenceLog

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

GetDataQualityMonitorDataProfilingConfigNotificationSettings

OnFailure GetDataQualityMonitorDataProfilingConfigNotificationSettingsOnFailure: (NotificationDestination) - Destinations to send notifications on failure/timeout

OnFailure GetDataQualityMonitorDataProfilingConfigNotificationSettingsOnFailure: (NotificationDestination) - Destinations to send notifications on failure/timeout

onFailure GetDataQualityMonitorDataProfilingConfigNotificationSettingsOnFailure: (NotificationDestination) - Destinations to send notifications on failure/timeout

onFailure GetDataQualityMonitorDataProfilingConfigNotificationSettingsOnFailure: (NotificationDestination) - Destinations to send notifications on failure/timeout

on_failure GetDataQualityMonitorDataProfilingConfigNotificationSettingsOnFailure: (NotificationDestination) - Destinations to send notifications on failure/timeout

onFailure Property Map: (NotificationDestination) - Destinations to send notifications on failure/timeout

GetDataQualityMonitorDataProfilingConfigNotificationSettingsOnFailure

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

GetDataQualityMonitorDataProfilingConfigSchedule

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

GetDataQualityMonitorDataProfilingConfigTimeSeries

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 v1.78.0 published on Friday, Nov 7, 2025 by Pulumi

Schema (JSON)

pulumi/pulumi-databricks

databricks.getDataQualityMonitor

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Example Usage

Using getDataQualityMonitor

getDataQualityMonitor Result

Supporting Types

GetDataQualityMonitorDataProfilingConfig

GetDataQualityMonitorDataProfilingConfigCustomMetric

GetDataQualityMonitorDataProfilingConfigInferenceLog

GetDataQualityMonitorDataProfilingConfigNotificationSettings

GetDataQualityMonitorDataProfilingConfigNotificationSettingsOnFailure

GetDataQualityMonitorDataProfilingConfigSchedule

GetDataQualityMonitorDataProfilingConfigTimeSeries

Package Details

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