---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: d83cf6d293ab0941b7246300ad7b58947648cb31a920dd2c85f718f163cebbdd
  pageDirectory: concepts
  sources:
    - create-a-profile-using-the-databricks-ui-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - metric-tables-and-profile-outputs
    - Profile Outputs and Metric Tables
    - MTAPO
  citations:
    - file: create-a-profile-using-the-databricks-ui-databricks-on-aws.md
title: Metric Tables and Profile Outputs
description: Unity Catalog tables automatically created by data profiling to store computed metrics, which can be queried via SQL, notebooks, or Catalog Explorer and controlled via Unity Catalog privileges.
tags:
  - data-governance
  - unity-catalog
  - outputs
timestamp: "2026-06-19T17:56:37.770Z"
---

# Metric Tables and Profile Outputs

**Metric Tables and Profile Outputs** refer to the structured tables and dashboards generated by [Data Profiling](/concepts/data-profiling.md) on Databricks. When you create a data profile on a Unity Catalog table, the system produces metric tables that store computed statistics and, for time series and inference profiles, a dashboard for visualizing results. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

## Metric Tables

Metric tables are [Unity Catalog](/concepts/unity-catalog.md) tables that contain summary statistics for each profiled column, time window, and slice. They are created automatically when a profile is configured and are stored in a schema you specify (defaulting to the same schema as the profiled table). ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

### Types of Metric Tables

The profiling system maintains two primary metric tables:

- **Profile Metrics Table** – Stores the computed statistics for each column across all time windows and slices.
- **Drift Metrics Table** – Stores statistics that track changes in distributions over time, enabling [Anomaly Detection](/concepts/anomaly-detection.md).

Although the source does not detail their schemas, both tables are queriable Unity Catalog tables and can be accessed in notebooks, the SQL query explorer, and Catalog Explorer. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

### Contents

The statistics stored in profile metric tables depend on the profile type and any custom metrics or slicing expressions you configure. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

- **Slicing expressions** – Define subsets of the data; each expression generates one slice for the predicate and one for its complement (or one slice per unique value for categorical columns). Metrics are computed separately for each slice. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]
- **Custom metrics** – User-defined SQL metrics (Aggregate, Derived, or Drift) that appear alongside built-in metrics in the tables. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

For a full description of available statistics, refer to the Databricks documentation on [Monitor metric tables](https://docs.databricks.com/aws/en/data-governance/unity-catalog/data-quality-monitoring/data-profiling/monitor-output). ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

## Initial Lookback Window for Time Series and Inference Profiles

When you first create a `TimeSeries` or `Inference` profile, the system analyzes **only data from the 30 days prior to creation**. After the initial run, all newly appended data is processed on each refresh. This means the first analysis window may be a partial window (e.g., if the 30-day boundary falls mid-week for a weekly granularity). Subsequent refresh windows are complete. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

## Dashboard

For `TimeSeries` and `Inference` profiles, a dashboard is automatically created to visualize the metric tables. The dashboard is owned by the user who created the profile and can be shared within the workspace using the **Share** button on the dashboard. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

## Access Control

Metric tables and dashboards are owned by the profile creator. Access to metric tables is controlled using standard [Unity Catalog](/concepts/unity-catalog.md) privileges. Dashboard visibility is managed through the workspace’s share permissions. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

## Performance Considerations

For `TimeSeries` and `Inference` profiles, it is a best practice to enable **change data feed (CDF)** on the profiled table. With CDF enabled, only newly appended rows are processed on each refresh instead of re‑reading the entire table, making execution more efficient and cost‑effective as profiling scales across many tables. ^[create-a-profile-using-the-databricks-ui-databricks-on-aws.md]

## Related Concepts

- [Data Profiling](/concepts/data-profiling.md)
- [Anomaly Detection](/concepts/anomaly-detection.md)
- [Unity Catalog](/concepts/unity-catalog.md)
- [Catalog Explorer](/concepts/catalog-explorer.md)
- [Inference Log Analysis](/concepts/inferencelog-analysis.md)
- Time Series Analysis
- [30-Day Lookback Window](/concepts/30-day-lookback-window.md)

## Sources

- create-a-profile-using-the-databricks-ui-databricks-on-aws.md

# Citations

1. [create-a-profile-using-the-databricks-ui-databricks-on-aws.md](/references/create-a-profile-using-the-databricks-ui-databricks-on-aws-d97351d2.md)
