data-quality-checks

id	SKL-data-DATAQUALITYCHECKS
name	Data Quality Checks
description	Data Quality Checks are automated tests that validate data against predefined rules and expectations. They act as the "unit tests" for data, catching issues before they propagate downstream to analyti
version	1.0.0
status	active
owner	@cerebra-team
last_updated	2026-02-22
category	Backend
tags	["api","backend","server","database"]
stack	["Python","Node.js","REST API","GraphQL"]
difficulty	Intermediate

Data Quality Checks

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Data Quality Checks are automated tests that validate data against predefined rules and expectations. They act as the "unit tests" for data, catching issues before they propagate downstream to analytics, ML models, or business decisions.

Core Principle: "Trust but verify. Every data pipeline should have quality gates."

This skill provides comprehensive guidance on implementing data quality checks across the data pipeline, from database constraints to ML-based anomaly detection.

Why This Matters

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• :
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Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • <e.g., env vars, request payload, file paths, schema>
• Entry Conditions:
  • <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
• Outputs:
  • <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
• Artifacts Required (Deliverables):
  • <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
• Acceptance Evidence:
  • <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
• Success Criteria:
  • <e.g., p95 < 300ms, coverage ≥ 80%>