---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: d8e9ae60a3807f727274c384c075fbc835a998d4852eac62aea0978c2c9a0f17
  pageDirectory: concepts
  sources:
    - data-profiling-dashboard-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - profile-analysis-types
    - PAT
  citations:
    - file: data-profiling-dashboard-databricks-on-aws.md
title: Profile analysis types
description: "Three types of data profiling analysis in Databricks: Snapshot, Timeseries, and InferenceLog, each with different dashboard selectors and default visualizations."
tags:
  - databricks
  - data-profiling
  - analysis-types
timestamp: "2026-06-18T15:00:24.433Z"
---

# Profile Analysis Types

**Profile analysis types** refer to the three modes of data profiling available in Databricks: Snapshot, Timeseries, and InferenceLog. Each type determines how metrics are computed and displayed, and the visualizations included in the default dashboard configuration depend on the profile type selected. ^[data-profiling-dashboard-databricks-on-aws.md]

## Overview

When a profile runs, it creates a dashboard that displays key metrics computed by the profile. The metrics are organized into sections, and the left side of the dashboard shows lists of the metrics and statistics included in the tables and charts. The dashboard has user-editable parameters for both the entire dashboard and for each chart, allowing customization of the date range, data slices, models, and so on. ^[data-profiling-dashboard-databricks-on-aws.md]

## Snapshot Analysis

Snapshot analysis captures the state of the data at a single point in time. This profile type provides a static view of data quality metrics for the moment the profile was run. The dashboard widgets for Snapshot analysis include specific selectors that control what data is displayed. ^[data-profiling-dashboard-databricks-on-aws.md]

## Timeseries Analysis

Timeseries analysis tracks data quality metrics over time, allowing users to observe trends and changes in their data across multiple profile runs. This profile type uses different selectors on the dashboard compared to Snapshot analysis, reflecting the temporal nature of the metrics. ^[data-profiling-dashboard-databricks-on-aws.md]

## InferenceLog Analysis

InferenceLog analysis is designed for monitoring the quality of data used in machine learning inference pipelines. Like Timeseries analysis, it uses distinct dashboard selectors that differ from those used in Snapshot analysis. ^[data-profiling-dashboard-databricks-on-aws.md]

## Dashboard Selectors

Each profile type presents different widgets at the top of the dashboard to control what data is included in the visualizations. The selectors vary by analysis type:

- **Snapshot**: Uses selectors appropriate for single-point-in-time analysis.
- **Timeseries**: Uses different selectors that support time-based filtering and trend visualization.
- **InferenceLog**: Uses distinct selectors tailored to inference monitoring workflows.

## Refreshing Profile Data

The metric tables and the dashboard generated by a profile are updated separately. When a profile refresh is triggered, the metric tables are updated, but the dashboard is not automatically updated. To update the data shown on the dashboard, users must click the **Refresh** button on the dashboard. ^[data-profiling-dashboard-databricks-on-aws.md]

Similarly, clicking **Refresh** on the dashboard does not trigger profile calculations. Instead, it runs the queries over the metric tables that the dashboard uses to generate visualizations. To update the data in the tables used to create the visualizations, users must refresh the profile and then refresh the dashboard. ^[data-profiling-dashboard-databricks-on-aws.md]

## Related Concepts

- [Data profiling dashboard](/concepts/data-profiling-dashboard.md) – The dashboard automatically created when a profile runs.
- [Data Quality Monitoring](/concepts/data-quality-monitoring.md) – The broader framework for monitoring data quality in Unity Catalog.
- [Unity Catalog](/concepts/unity-catalog.md) – The data governance platform that supports data profiling.
- [Catalog Explorer](/concepts/catalog-explorer.md) – The UI for navigating tables and accessing profile dashboards.

## Sources

- data-profiling-dashboard-databricks-on-aws.md

# Citations

1. [data-profiling-dashboard-databricks-on-aws.md](/references/data-profiling-dashboard-databricks-on-aws-7be46e44.md)
