---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 4ac6fbd363af3f84b888b2f11f74bd2614a45a838fe115d769a664ab0dabcf21
  pageDirectory: concepts
  sources:
    - data-profiling-metric-tables-databricks-on-aws.md
  confidence: 0.98
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - databricks-profile-metrics-30-day-lookback-window
    - DPM3LW
  citations:
    - file: data-profiling-metric-tables-databricks-on-aws.md
title: Databricks Profile Metrics 30-Day Lookback Window
description: For TimeSeries and InferenceLog profiles, metrics look back 30 days from profile creation time, which may cause partial windows (e.g., mid-week/month boundaries) in the first analysis.
tags:
  - data-profiling
  - time-windows
  - databricks
timestamp: "2026-06-19T09:45:22.127Z"
---

# Databricks Profile Metrics 30-Day Lookback Window

The **Databricks Profile Metrics 30-Day Lookback Window** is a temporal boundary applied during the computation of profile statistics for `TimeSeries` and `InferenceLog` analysis types. When a profile is created or refreshed, the system looks back **30 days from the time the profile is created** to collect data for metric calculations. This lookback window affects both the [Profile Metrics Table](/concepts/profile-metrics-table.md) and the [Drift Metrics Table](/concepts/drift-metrics-table.md). ^[data-profiling-metric-tables-databricks-on-aws.md]

## How It Works

For `Snapshot` analysis, the time window is a single point in time (the moment the metric is refreshed). For `TimeSeries` and `InferenceLog` analysis, the time window is derived from the granularities specified in the `create_monitor` call and the `timestamp_col` values. The system then computes metrics for each time window, but **only over the most recent 30 days** from the profile creation time. ^[data-profiling-metric-tables-databricks-on-aws.md]

No data older than 30 days at the time of profile creation is included in any window’s statistics. This is a fixed cutoff, not a sliding window that ages with each refresh; the 30‑day period is always measured from the time the profile is created (or recreated). ^[data-profiling-metric-tables-databricks-on-aws.md]

## Implications for the First Analysis Window

Because the profile looks back exactly 30 days, the **first analysis window may be partial**. For example, if the 30‑day cutoff falls in the middle of a week or month, that week or month is **not fully included** in the calculation. This partial window effect occurs **only for the first window** after a profile is created. Subsequent windows align fully with the specified granularity (e.g., complete weeks or months). ^[data-profiling-metric-tables-databricks-on-aws.md]

## Affected Metric Tables

The 30‑day lookback window applies to both metric tables generated by data profiling:

- **Profile metrics table**: Contains summary statistics (counts, nulls, distinct values, quantiles, etc.) for each column, computed per time window. See the [profile metrics table schema](#profile-metrics-table-schema) in the source material for the full list of columns.
- **Drift metrics table**: Contains drift statistics that compare distributions between consecutive windows or against a baseline. The lookback window affects which data is available for drift calculations.

In both tables, the lookback constraint is documented in the schema notes (footnotes [2] and [7]) for time‑series and inference profiles. ^[data-profiling-metric-tables-databricks-on-aws.md]

## Related Concepts

- [Data Profiling](/concepts/data-profiling.md) – The process that generates profile and drift metrics.
- TimeSeries Analysis – One of the analysis types subject to the 30‑day lookback.
- [InferenceLog Analysis](/concepts/inferencelog-analysis.md) – Another analysis type subject to the 30‑day lookback.
- [Profile Metrics Table](/concepts/profile-metrics-table.md) – The output table containing per‑column statistics.
- [Drift Metrics Table](/concepts/drift-metrics-table.md) – The output table tracking distribution changes.
- [Consecutive Drift](/concepts/consecutive-drift.md) – Compares a window to the previous window; affected by the lookback.
- [Baseline Drift](/concepts/baseline-drift.md) – Compares a window to a baseline distribution; also affected.

## Sources

- data-profiling-metric-tables-databricks-on-aws.md

# Citations

1. [data-profiling-metric-tables-databricks-on-aws.md](/references/data-profiling-metric-tables-databricks-on-aws-8eea6c26.md)
