Skip to main content

fusion-bicc-data-drift-detect

Detect data drift within Oracle Fusion BICC extracted objects by comparing profile snapshots over time and rate findings with criticality and remediation guidance.

Zur Installation springen

Quellinformationen

Repository
oracle-samples/fusion-ai-skills
Letzte Quellaktivität
4. September 2026 um 17:06
Erkannte Sprache von SKILL.md
Englisch
Sterne
5
Forks
1

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

Datei-Explorer
9 Dateien

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
fusion-bicc-data-drift-detect
description
Detect data drift within Oracle Fusion BICC extracted objects by comparing profile snapshots over time and rate findings with criticality and remediation guidance.
compatibility
Requires Python 3.10+; optional PyYAML for YAML policy files; CSV input and JSON outputs.
metadata
{"author":"fusion-data-architecture","version":"1.0.0"}
allowed-tools
Bash(python:*) Read Write
## Copyright (c) 2026, Oracle and/or its affiliates. ## Licensed under the Universal Permissive License v 1.0 as shown at http://oss.oracle.com/licenses/upl # Fusion BICC Data Drift Detection Use this skill when schema appears stable but data behavior changes across extraction windows and can impact KPIs, model quality, or ODT load quality. ## When to use - Null rates spike after a Fusion release. - Distinct cardinality unexpectedly collapses or expands. - Measure ranges/means shift materially. - You need severity-rated drift findings with remediation actions. ## Inputs - Baseline and current BICC CSV extracts. - Data drift policy (`assets/data_drift_policy.yaml`). ## Workflow 1. Build baseline data profile snapshot from known-good extraction window. 2. Build current data profile snapshot for the latest run. 3. Run drift detection with policy thresholds. 4. Review critical findings and associated remediation in report outputs. ## Commands ```bash # 1) Build profile snapshots python skills/fusion-bicc-data-drift-detect/scripts/build_data_profile_snapshot.py \ --input-dir ./inputs/baseline \ --output ./outputs/baseline_data_profile.json python skills/fusion-bicc-data-drift-detect/scripts/build_data_profile_snapshot.py \ --input-dir ./inputs/current \ --output ./outputs/current_data_profile.json # 2) Detect data drift python skills/fusion-bicc-data-drift-detect/scripts/detect_data_drift.py \ --baseline ./outputs/baseline_data_profile.json \ --current ./outputs/current_data_profile.json \ --policy skills/fusion-bicc-data-drift-detect/assets/data_drift_policy.yaml \ --output-json ./outputs/data_drift_report.json \ --output-md ./outputs/data_drift_report.md ``` ## Outputs - `data_drift_report.json` - `data_drift_report.md` ## Critical examples - Required field null ratio exceeds critical threshold. - Row count drops below expected freshness/completeness threshold. - Categorical domain shifts beyond policy tolerance. See `references/data-drift-metrics.md` and `references/severity-model.md`.
Auf GitHub ansehen