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Profiling Guide
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Data Profiling (DTE Pattern)

Mission

Profiling is how hidden risks become visible. In Data Trust Engineering (DTE), profiling is not a one‑off audit—it’s a lightweight, continuous signal that feeds confidence indicators and trust dashboards.

Quick Start: Profile a Dataset

# profiling.py
import pandas as pd
from ydata_profiling import ProfileReport

# Load dataset
df = pd.read_csv("user_data.csv")

# Generate profile
profile = ProfileReport(df, title="User Data Profiling")
profile.to_file("artifacts/profiling/user_data_profile.html")

print("Profile written to artifacts/profiling/user_data_profile.html")

Run:

python profiling.py

Design Patterns for Profiling

1) Confidence Indicators

  • Derive trust metrics from profiling: completeness %, duplication %, freshness.
  • Publish indicators alongside dashboards.

2) Continuous Profiling

  • Run profiling as part of CI/CD (sampled data).
  • Store artifacts (.html, .json) in evidence packs.

3) Profiling as Baseline

  • Use profiles as reference points for drift detection.
  • Compare current vs. baseline profiles.

4) Lightweight Metrics

  • Track only a few critical metrics: null %, cardinality, skewness.
  • Avoid full audits in every run.

Minimal Metrics to Capture

  • Completeness % — non‑null values per column.
  • Duplication % — duplicate rows or keys.
  • Freshness — max timestamp age.
  • Skewness — distribution anomalies.

Open‑Source Toolkit

Profiling feeds confidence indicators, enabling teams to see problems before they erode trust.