Qlik Sense data modeling
Master Qlik Sense scripting and data modeling principles
About Qlik Sense Data Modeling Training
A complete, end-to-end Qlik Sense scripting training built for team autonomy
An intensive training to master the Qlik Sense scripting language and data modeling fundamentals, enabling participants to build robust, high-performance data marts.
In this training, participants work in the Data Load Editor: connecting to multiple sources, turning relational models into analysis-ready data marts (star schema), resolving common modeling issues (synthetic keys, circular references), and finalizing robust models with governance, performance, and security (Section Access).
By the end of these 3 days, participants can design, build, debug, and maintain Qlik Sense data models end-to-end with confidence and autonomy.
The eaQbe Methodology: A Progressive Learning Curve
We don’t believe in training that overwhelms participants with theory. We believe in structured immersion. Our “3-Stage Autonomy Arc” ensures concepts aren’t just understood ; they’re truly mastered.
1) Foundations & Data Load Editor Mastery
Before modeling, participants get comfortable with the environment and scripting fundamentals: Data Load Editor components, connections, and source data structures. They learn how data is extracted and loaded (LOAD statements, SQL SELECT), how to monitor reload progress, and how to control script execution. They also learn to analyze the model in the Data Model Viewer, spot common pitfalls early (synthetic keys, circular references), and apply the right patterns to fix them.
2) Structuring the Data Mart
Building on the loaded data, participants learn how to clean, enrich, and transform the model in script: calculated fields, Resident/Inline loads, limiting and reusing data, composite keys, and a master calendar. They then apply the patterns that make a data model reliable and simpler- mapping tables, preceding loads, joins, and concatenation - to resolve common issues (synthetic keys, circular references, grain mismatches) and reduce unnecessary tables. The goal is a clean, analysis-ready star schema that remains maintainable over time.
3) Optimize, Govern & Secure
Participants learn how to finalize models for real-world use: performance patterns, QVD strategy, incremental loads, and handling large volumes through aggregation tables and ODAG when relevant. They implement durable governance (model discipline and key controls) and secure access with Section Access (including dynamic reductions). The result: faster reloads, more stable apps, and data models that remain maintainable over time.
Continuous knowledge validation
Each module intentionally reuses and builds on concepts from the previous one. Participants apply earlier patterns in new scenarios, so mastery grows naturally and consistently throughout the 3 days.
Learning outcomes
- Operational autonomy in Qlik Sense scripting: connect data sources, transform data, model, diagnose, and maintain data flows in the Data Load Editor.
- Star schema modeling mastery: build reliable data marts optimized for analytics and consistent over time.
- Modeling issue resolution: handle synthetic keys, circular references, grain mismatches, and complex edge cases.
- Performance & governance: implement a QVD strategy, incremental loads, and performance patterns (aggregations / ODAG when relevant).
- Access security: implement aSection Access and responsible data reduction patterns, and master the basics of the qlik management console
Practical details
- Format: 3-day intensive workshop (interactive & scenario-based)
- Group size: 5–10 participants
- Prerequisites: none
- Follow-up: evaluation + practical exercises to anchor post-training
From sources to star schema: Qlik Sense Data Modeling (3 days)
A scenario-driven program to master the Qlik Sense scripting language and data modeling principles, turning relational data into reliable, high-performance, secure data marts.
Module 1 - Foundations: analytics model & Data Load Editor
- What makes a model “analysis-ready”: facts vs dimensions, keys, grain
- Why star schema—and how to target it in Qlik
- Data Load Editor basics: connections, extraction (LOAD / SQL), reload monitoring
- Table & model analysis in the Data Model Viewer
- Spotting common issues early: synthetic keys, circular references, ambiguity
Module 2 - Transforming & enriching in script
- Cleaning and shaping data: calculated fields, standardization, rules
- Structuring flows: Resident/Inline loads, intermediate tables
- Building consistent keys (including composite keys)
- Creating a robust master calendar
- Validation habits: checks, counts, consistency, traceability
Module 3 - Structuring the data mart (star schema)
- Simplifying the model: reducing unnecessary tables, clarifying relationships
- Structuring patterns: mapping tables, preceding loads, joins, concatenation
- Fixing breaking points: synthetic keys, circular references, grain mismatches
- Delivering a clean, stable, BI-ready star schema
Module 4 - Performance & large-volume handling
- QVD strategy: layers, storage, reload organization
- Incremental loads: patterns and pitfalls
- Aggregation tables: speeding up analytics on large datasets
- ODAG (when relevant): when to use it and how to frame it
Module 5 - Governance & security
- Model discipline: conventions, key controls, team maintenance
- Section Access: principles, implementation, responsible dynamic reductions
- QMC basics (depending on scope): publishing, management, watch-outs
By the end of the 3 days, Participants leave with a clear method to design, maintain, and evolve Qlik Sense data models in real-world conditions.
