qlik-data-modeling

Design Qlik star-schema data models with synthetic-key prevention.

1|Updated Mar 13, 2026
One-click install
npx skills add https://github.com/Pupfish-LLC/qlik-agents --skill qlik-data-modeling
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qlik-data-modeling
Source: https://github.com/Pupfish-LLC/qlik-agents/tree/main/skills/qlik-data-modeling
Command: npx skills add https://github.com/Pupfish-LLC/qlik-agents --skill qlik-data-modeling

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Qlik data modeling is often hampered by synthetic keys, grain misalignment, and monolithic architectures. This skill provides a structured playbook to design, validate, and evolve star-schema driven models that are scalable, maintainable, and compatible with multi-app deployments.

Core Features & Use Cases

  • Star-schema design patterns: guidance on fact/dimension architecture, grain alignment, and cross-app reuse.
  • Synthetic key prevention: entity-prefixing, bridge tables, ApplyMap, and explicit column listing.
  • QVD layering & multi-app patterns: Raw/Transform/Model layers and generator/consumer architectures for shared data services.
  • Adaptability to source architectures: strategies for Dimensional Warehouses, OLTP, Data Vault 2.0, and pre-joined views.

Quick Start

Review the star-schema patterns and implement a starter model in a test project using the recommended approaches.

Frequently Asked Questions about qlik-data-modeling

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

FAQPage Schema
How do I prevent synthetic keys when building a Qlik data model?

Prevent synthetic keys in your Qlik data model by using entity-prefixing, bridge tables, ApplyMap functions, and explicit column listing instead of relying on automatic key resolution. These strategies ensure clean associations and prevent unwanted synthetic key generation.

What is the best way to structure a multi-app Qlik data architecture?

The best way to structure multi-app Qlik architectures is by implementing QVD layering with Raw, Transform, and Model layers alongside generator and consumer applications. This pattern provides shared data services for scalable cross-source consumption.

How do I design a star schema for Qlik that supports cross-app reuse?

Design a Qlik star schema by defining fact and dimension tables, aligning data grains, and applying disciplined naming conventions. This architecture ensures dimensional tables can be reused effectively across multiple Qlik applications.

Can I use bridge tables to resolve grain misalignment in Qlik?

Yes, you can use bridge tables to resolve grain misalignment in Qlik. Bridge or link tables connect dimensions to facts at different granularities, preventing synthetic keys while maintaining accurate analytical associations across your data model.

Does Qlik data modeling work with Data Vault 2.0 and OLTP sources?

Yes, Qlik data modeling adapts to various source architectures including Data Vault 2.0, OLTP systems, Dimensional Warehouses, and pre-joined views. The approach guides star-schema design strategies specific to each source system's structure.

Why does my Qlik data model create circular references and how do I fix it?

Circular references in Qlik data models typically occur due to grain misalignment or unmanaged table associations. Fix them by implementing bridge tables, ApplyMap, and explicit key-resolution strategies to isolate multiple fact tables and enforce a clean star schema.