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datamart-review

Validate Datamart HLD and LLD designs against business analyst documentation.

1|Updated Apr 7, 2026
Search Tags:#data-modeling#business-logic#data-quality#design-review#datamart#hld-lld
One-click install
npx skills add https://github.com/linhlvit/ubck_atomic_design --skill datamart-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datamart-review
Source: https://github.com/linhlvit/ubck_atomic_design/tree/main/.claude/skills/datamart-review
Command: npx skills add https://github.com/linhlvit/ubck_atomic_design --skill datamart-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Mismatches between business analyst (BA) requirement documentation and Datamart high-level (HLD) and low-level (LLD) designs lead to incorrect reports, dashboards, and data products that do not align with business needs. This Skill eliminates that risk by enforcing end-to-end traceability from source data through Atomic entities to final Datamart outputs.

Core Features & Use Cases

  • 3-Layer Cross-Check: Validates HLD coverage, Atomic-to-Datamart attribute mapping, and Datamart-to-reporting detail mapping for every group in a module.
  • Issue Classification: Automatically categorizes gaps into 3 scenarios: missing HLD when BA is complete, logic mismatches from BA updates, and technical design errors, with clear action recommendations.
  • Use Case: A data modeler working on the UBCK Lakehouse project can use this Skill to review an entire Datamart module, catch missing KPIs, incorrect ETL logic, or misaligned filters before deployment, ensuring all reporting outputs match BA requirements.

Quick Start

Use the datamart-review skill to cross-check the BA analyst documentation and Datamart HLD/LLD files for the 'customer_reporting' module to identify logic gaps and design inconsistencies.

Frequently Asked Questions about datamart-review

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

FAQPage Schema
How do I validate Datamart design consistency against BA requirement documentation?▼

To validate Datamart design consistency against BA documentation, cross-check high-level and low-level designs to ensure end-to-end traceability from source data to reporting outputs, eliminating mismatches in business logic.

What is the best way to trace Atomic-to-Datamart attribute mapping for data modeling?▼

Tracing Atomic-to-Datamart attribute mapping involves a 3-layer cross-check validating HLD coverage, source-to-Atomic mapping, and Datamart-to-reporting detail mapping to catch incorrect ETL logic or misaligned filters.

How do I categorize data modeling gaps between HLD and BA logic updates?▼

Categorize data modeling gaps into three scenarios: missing HLD when BA is complete, logic mismatches from BA updates, and technical design errors, providing clear action recommendations for each issue.

Can I review Datamart module groups sequentially to catch missing KPIs?▼

Yes, you can review Datamart module groups sequentially by enforcing strict gate rules for individual module groups, ensuring missing KPIs and design inconsistencies are caught before deployment.

Does the Datamart design review process require user confirmation before modifying HLD and LLD files?▼

Yes, the Datamart design review process enforces mandatory user confirmation before executing any file modifications or skill invocations, ensuring controlled updates to HLD and LLD designs.

When do I need to perform a 3-layer cross-check for Datamart reporting outputs?▼

Perform a 3-layer cross-check for Datamart reporting outputs when mismatches between BA requirements and technical designs risk incorrect dashboards, ensuring all reporting outputs match business needs.

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