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datamart-lld-design

Generate compliant Datamart Low-Level Design artifacts with validated CSV and SQL outputs.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates inconsistent, error-prone manual Low-Level Design (LLD) for Datamart layers in data warehouse projects, ensuring alignment with approved High-Level Design (HLD) and Atomic data models to avoid costly rework and schema mismatches.

Core Features & Use Cases

  • 3-Phase Structured Workflow: Guides data modelers through Phase 1 (Attributes CSV generation), Phase 2 (KPI Detail Mapping), and Phase 3 (ClickHouse flat table SQL generation) with strict, mandatory gate checks between phases.
  • Built-in Validation: Includes 5 mandatory self-review test cases for Phase 1, 4 for Phase 2, and cross-checks against HLD, Atomic entity YAML files, and BA requirements to catch mapping errors before delivery.
  • Use Case: For UBCKNN lakehouse project data modelers, this Skill automates LLD generation for Datamart modules while enforcing governance rules for source system code filtering, ETL logic formatting, and key constraint compliance.

Quick Start

Provide the approved HLD and Entities files for your target Datamart module to generate a compliant Phase 1 Attributes CSV with validated ETL source mappings.

Frequently Asked Questions about datamart-lld-design

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

FAQPage Schema
How do I generate a compliant Datamart LLD from an approved HLD document?▼

To generate a Datamart LLD, provide the approved HLD and Atomic layer entity files to a 3-phase workflow that produces validated attribute CSVs, KPI mappings, and ClickHouse SQL. This ensures strict HLD alignment and eliminates manual schema mapping errors.

What is the best way to create ClickHouse flat table SQL for a Datamart layer?▼

The best way to create ClickHouse flat table SQL is using a phased LLD workflow that cross-checks KPI detail mappings against Atomic layer entities. This enforces mandatory gate checks and key constraint compliance before generating the final SQL scripts.

How do you validate ETL source mappings in a Datamart LLD?▼

You validate ETL source mappings in a Datamart LLD by applying built-in validation rules and mandatory self-review test cases across the workflow phases. This cross-checks attribute CSVs and KPI mappings against HLD and Atomic entity YAML files to catch errors early.

Do I need Atomic layer entity models to start Datamart LLD generation?▼

Yes, you need Atomic layer entity models and an approved HLD document to start Datamart LLD generation. These inputs are mandatory for Phase 1 attribute CSV generation and ensure forward-compatible source system code filtering and schema alignment.

Why does Datamart LLD generation require mandatory gate checks between phases?▼

Datamart LLD generation requires mandatory gate checks between phases to prevent inconsistent schema definitions and manual mapping errors. Skipping these checks risks schema mismatches with the Atomic layer and costly downstream rework in data warehouse projects.

Can I use this Datamart LLD workflow for source system code filtering?▼

Yes, you can use this Datamart LLD workflow for source system code filtering. It enforces governance rules for ETL logic formatting and forward-compatible source code filtering to ensure production-ready, maintainable Datamart implementations.

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