---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: d95404740df8f75532d3efe46ef05a6f1414c0098c48b24f7d5bc5b042ac1627
  pageDirectory: concepts
  sources:
    - profile-alerts-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - profile-alerts
    - Profile Alert
  citations:
    - file: profile-alerts-databricks-on-aws.md
title: Profile Alerts
description: Databricks SQL alerts configured on profile metrics tables to notify when data statistics move out of expected ranges or when data drift is detected.
tags:
  - databricks
  - data-quality
  - alerting
  - unity-catalog
timestamp: "2026-06-19T19:57:45.898Z"
---

# Profile Alerts

**Profile alerts** are Databricks SQL alerts configured on top of profile metrics tables or drift metrics tables, enabling automated monitoring and notification when data quality or distribution statistics move outside expected ranges.

## Overview

Profile alerts allow you to receive notifications when specific statistical measures in your [Data Profiling](/concepts/data-profiling.md) analysis change or fall outside defined thresholds. These alerts leverage the same infrastructure as standard Databricks SQL alerts, but are specifically designed to work with the metrics generated by data profiling operations.^[profile-alerts-databricks-on-aws.md]

### Common Use Cases

- **Out-of-range statistics**: Get notified when a statistic moves outside a certain range. For example, you can configure an alert when the fraction of missing values exceeds a specific threshold.^[profile-alerts-databricks-on-aws.md]

- **Data distribution changes**: Monitor the [Drift Metrics Table](/concepts/drift-metrics-table.md) which stores statistics tracking changes in the data distribution over time.^[profile-alerts-databricks-on-aws.md]

- **Baseline drift detection**: Set up alerts to investigate when data has drifted compared to the [Baseline Table](/concepts/baseline-table.md). For [Inference Log Analysis](/concepts/inferencelog-analysis.md), this can indicate that the model should be retrained.^[profile-alerts-databricks-on-aws.md]

## How Profile Alerts Work

Profile alerts are created and used the same way as other Databricks SQL alerts. The process involves two main steps:^[profile-alerts-databricks-on-aws.md]

1. **Create a query**: Build a Databricks SQL query that queries the [Profile Metrics Table](/concepts/profile-metrics-table.md) or [Drift Metrics Table](/concepts/drift-metrics-table.md) for the specific metric you want to monitor.

2. **Create an alert**: Configure a Databricks SQL alert on that query, specifying the conditions under which the alert should trigger.

## Configuration

### Evaluation Frequency

You can configure the alert to evaluate the query at a desired frequency. This allows you to control how often the system checks your metrics against the defined thresholds.^[profile-alerts-databricks-on-aws.md]

### Notification Channels

When an alert is triggered, notifications can be sent through multiple channels:^[profile-alerts-databricks-on-aws.md]

- **Email**: Default notification method, automatically configured for all alerts.
- **Webhook**: Send notifications to custom endpoints for integration with other systems.
- **Slack or PagerDuty**: Configure alerts to send notifications to team communication and incident management tools.

### Query Parameters

If the query uses parameters, the alert is based on the default values for those parameters. It is important to confirm that the default values reflect the intent of the alert before saving.^[profile-alerts-databricks-on-aws.md]

## Related Concepts

- [Data Profiling](/concepts/data-profiling.md) – The statistical analysis that generates the metrics used in profile alerts.
- [Profile Metrics Table](/concepts/profile-metrics-table.md) – Stores summary statistics for each column, time window, and slice.
- [Drift Metrics Table](/concepts/drift-metrics-table.md) – Stores statistics tracking distribution changes over time.
- [Baseline Table](/concepts/baseline-table.md) – An optional reference table used to compute baseline drift.
- [Inference Log Analysis](/concepts/inferencelog-analysis.md) – A profile type for monitoring model predictions, where alerts can indicate model retraining needs.
- Time Series Analysis – A profile type for monitoring data over continuous time windows.
- [Data Slicing](/concepts/data-slicing-expressions.md) – Metrics are computed for data slices, enabling targeted alerting.

## Sources

- profile-alerts-databricks-on-aws.md

# Citations

1. [profile-alerts-databricks-on-aws.md](/references/profile-alerts-databricks-on-aws-08d2e777.md)
