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Sir-chawakorn
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diff-table-parity

Compare two datasets for row counts, key differences, and column value mismatches.

9|3|Updated Jun 13, 2026
Search Tags:#database#migration#etl#quality-assurance#parity#data-validation
One-click install
npx skills add https://github.com/Sir-chawakorn/sanook-cli --skill diff-table-parity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: diff-table-parity
Source: https://github.com/Sir-chawakorn/sanook-cli/tree/main/skills/diff-table-parity
Command: npx skills add https://github.com/Sir-chawakorn/sanook-cli --skill diff-table-parity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill resolves the ambiguity in data validation by providing a rigorous, step-by-step framework to compare two datasets, identifying exactly where and why they differ beyond simple row counts.

Core Features & Use Cases

  • Schema Reconciliation: Identifies column mismatches, type differences, and missing fields between two datasets.
  • Granular Diffing: Performs row-set and column-value comparisons using join keys to pinpoint specific value drifts or defects.
  • Use Case: Use this when validating a data migration between a legacy database and a new system to ensure that every record matches or to document expected discrepancies like rounding or formatting.

Quick Start

Use the diff-table-parity skill to compare the source table users_v1 and the target table users_v2 using the user_id column as the primary join key.

Frequently Asked Questions about diff-table-parity

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate data migration parity between two database tables?▼

Data migration validation compares source and target datasets by analyzing row counts, key set differences, and per-column value mismatches using deterministic join keys and null-safe logic to produce a definitive parity verdict.

What is the best way to perform ETL regression testing on row sets?▼

ETL regression testing requires granular diffing that performs row-set and column-value comparisons using join keys to pinpoint specific value drifts or defects, resolving ambiguity by identifying exactly where and why datasets differ.

How do I compare two datasets to find specific value drifts after a query refactor?▼

To find value drifts after a query refactor, execute granular diffing that performs row-set and column-value comparisons using join keys, identifying schema mismatches and specific value drifts beyond simple row counts.

Do I need deterministic join keys for null-safe data validation?▼

Yes, deterministic join keys are required for null-safe data validation. The parity verification process depends on these stable keys to accurately compare row sets and produce a definitive verdict without matching errors.

Can I document expected discrepancies like rounding or formatting during schema reconciliation?▼

Yes, during schema reconciliation you can identify column mismatches, type differences, and missing fields, while using the granular diffing results to document expected discrepancies like rounding or formatting variations.

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