Preparing Your Aident Workspace...

Cas d’usageTarifs Documentation

FRCommencer maintenant

AppsDatabricks ToolsDatabricks Qualitymonitorv2 Quality Monitor V2 Create

Databricks Tools

Databricks Qualitymonitorv2 Quality Monitor V2 Create

Utilisez Databricks Qualitymonitorv2 Quality Monitor V2 Create avec Databricks Tools via Aident Loadout. Vérifiez les entrées, sorties, la configuration, le tarif et le risque avant exécution.

Action d'écritureNiveau de risque 2 sur 5

Produit

Aident Loadout Aident Playbook Editor Open-CUAK

À propos

Apps Skills Cas d’usage Documentation Blog Tarifs Téléchargements Politique de confidentialité Conditions d'utilisation

Alternatives

vs Composio vs Zapier

llms.txtObtenir un résumé IA d’Aident

© Aident Labs, 2024-2026

Connexion par clé API

Utiliser Databricks Qualitymonitorv2 Quality Monitor V2 Create

Description du fournisseur

Tool to create a quality monitor for Unity Catalog table. Use when you need to set up monitoring for data quality metrics, track drift over time, or monitor ML inference logs. Monitor creation is asynchronous; dashboard and metric tables take 8-20 minutes to complete. Exactly on…

Tarifs et limites

Tarif: Action gratuite
Paiement unifié: Non pris en charge actuellement

Entrées

scheduleobject
Cron schedule configuration for automatic monitor refresh.
snapshotobject
Snapshot table monitoring configuration.
assets_dirstring
Obligatoire
Absolute workspace path for storing monitoring assets including dashboard and metric tables (e.g., /Workspace/Users/{email}/monitoring/)
table_namestring
Obligatoire
Full name of Unity Catalog table to monitor in format catalog.schema.table_name (case insensitive, spaces disallowed)
time_seriesobject
Time series monitoring configuration.
warehouse_idstring
Warehouse ID for dashboard query execution
inference_logobject
Inference log monitoring configuration for ML models.
notificationsobject
Notification settings for quality monitor alerts.
slicing_exprsarray
Column expressions for slicing data to enable targeted analysis on data segments
custom_metricsarray
User-defined custom quality metrics for specialized monitoring needs
output_schema_namestring
Obligatoire
Schema where output metric tables are created in 2-level format {catalog}.{schema}
baseline_table_namestring
Baseline table name for computing drift metrics in format catalog.schema.table_name

Sortie observable

data
Obligatoire
Data from the action execution
errorstring
Error if any occurred during the execution of the action
successfulboolean
Obligatoire
Whether or not the action execution was successful or not

Skills qui utilisent cette Action

Cas d’usage associés

Aucun cas d’usage associé et vérifié pour le moment.

Source officielle

Source du fournisseur

Skills qui utilisent cette Action

Aucune Skill publiée ne référence encore explicitement cette Action.