Qlik Script & Data Modeling Best Practices: Notes from the Field

Working with Qlik in a real-world setting — especially in healthcare data and ETL-heavy environments — you quickly realize that clean script structure and thoughtful modeling can make or break your workflow. Online, you’ll find tons of technical “best practices,” but I wanted to share something a bit more grounded: what’s actually worked for me, in live environments, under real pressure.

This list mixes standard wisdom with my own habits and small tricks that have genuinely made life easier when maintaining, handing off, or revisiting Qlik projects.

Think Before You Script: Data Modeling Comes First

Before opening the script editor, take time to sketch out your data model. In Qlik, data modeling isn’t just backend logic — it is the logic. And if you skip thinking through your model, you’ll end up writing spaghetti scripts trying to fix structure problems later.

Some key points:

• Aim for a star schema.
  Facts in the center, dimensions around them. It’s not SQL — don’t over-normalize.
  Less is more when it comes to joins in Qlik.
• Use a Link Table only when needed.
  Don’t rush into creating a LinkTable just because you have shared keys. Check:
  – Does the granularity align?
  – Will it cause row duplication?
  – Can I use concatenation instead?
• Be picky about your keys.
  Composite keys are fine if they make sense — but only if you fully control them. And always check for synthetic keys. If one appears, pause and fix the model before continuing.
• Clean your field names as early as possible.
  Use a naming convention (e.g., ATC, Dosage) if it helps — especially in collaborative projects.
• Use QUALIFY with intention, not as a quick fix.
  It’s great for avoiding field name collisions, but it also hides your logic. Avoid qualifying everything blindly. Instead, isolate it to specific loads where you need it.

Before Anything Else: Add a Boilerplate

Right at the very top of your script — before Main, before variables — add a boilerplate comment. It sounds basic, but trust me, it makes the context of your app instantly clear.

Here’s the format I personally use:

It’s old-school, but immediately helpful. Anyone reading the script knows what they’re dealing with. No guessing.

Variabilize Your Paths — Future You Will Thank You

One of the best small decisions I made was to define all my paths as variables right at the start of the script. It brings consistency, makes refactoring painless, and avoids typos or outdated folder names scattered across the code.

Here’s a typical example from one of my apps:

SET path_conn = lib://QlikData/ETL;
SET path_associative = $(path_conn) /QV_Associative;

Need to switch environments? Just change one variable. Need to reuse a path? It’s already named. Need to pass this to a teammate or DevOps? It’s readable and editable in one place.

Also, I usually define control variables here too:

SET loadBudget = 0; // Economic data fetch
SET CreateIndicator = 1; // Toggle for Indicator.qvd creation

This setup at the top of Main gives you a control center to flip switches for data processing tasks. Especially helpful in ETL apps where reprocessing everything is expensive.

Small thing, big win.

Build a CONTROL FLOW Tab (Yes, All Caps)

At the end of your script, I always recommend adding a tab called CONTROL FLOW. It’s basically a checklist and visual cue for the execution order.

// CONTROL FLOW
CALL FetchData;
CALL CreateLink;
CALL MasterCalendar;
CALL GenerateKPI;

It becomes your script’s roadmap. When revisiting a project after a few months — or passing it to someone else — it’s gold.

Let Your Script Tell a Story — Clearly

Break down your script into tabs by logic or output — one tab per major table or subroutine. Think of each section as a small, self-contained story.

Use clear, minimal comments. Don’t annotate every field, but explain why a particular logic or calculation exists if it’s not immediately obvious.

Watch Those Joins — They Bite

Be extremely careful when joining tables, especially in healthcare or pharma datasets where granularity is strict and overlaps are subtle.

Always ask:

• Am I duplicating rows?
• Is this duplication expected — or a red flag?
• Is this join really necessary at this step?

Test the record counts before and after joins. You’ll catch issues early. If there’s duplication, decide: is this a many-to-many relationship I need to flatten? Or is something broken?

Avoid Breaking the Optimized Load

Loading from QVDs with conditions (like WHERE) can break optimized loads — unless you’re using WHERE EXISTS. This can drastically slow performance. Always check if you can filter after the load, or by using a semi-join/mapping table instead.

Don’t Hardcode Dates, Paths, or Flags

Instead of writing:

LET vStartYear = 2021;

Use logic to define these values dynamically whenever possible:

LET vToday = Today();
LET vStartYear = Year(vToday) - 2;

Hardcoded values almost always become technical debt.

Keep the Data Model Clean

• Hide helper fields with HidePrefix (e.g. _)
• Exclude backend fields from Smart Search
• Tag date and ID fields properly
• Keep key fields at the top of your tables for clarity
• Use Comment Fields for auto-documentation, especially for output QVDs

Use the model viewer often to ensure you’re not ending up with unintended synthetic keys or circular joins.

Use Master Calendars — Not Just Date Fields

A proper calendar with Year, Month, Week, ISO Week, Quarter, and Weekday will always come in handy. Don’t wait until the dashboard phase to realize your date field needs transformation.

Add fiscal periods if needed. Better now than mid-sprint.

Limit External Dependencies

Don’t go overboard with INCLUDE files or custom variables unless there’s a good reason. It’s tempting to abstract too much, but it can make debugging harder when you’re on a deadline.

Be Consistent With Script Styling

Whether you use square brackets or apostrophes for field names, just pick one and stick to it. Align your indentation. Use uppercase for keywords (LOAD, JOIN, RESIDENT), lowercase for variables and table names. It adds a quiet clarity to your work.

Other Quick Wins

• Use QUALIFY carefully — don’t let your field names explode without control
• Pre-calculate KPIs in the script if they’re heavy in the front-end
• Use mapping tables over repeated IF/ELSE logic for cleaner code

Final Thoughts

At the end of the day, these are just tools to help you build better apps with fewer headaches. You don’t need to apply everything at once. Pick a few that resonate and slowly fold them into your team’s style.

For me, having a clean data model, a boilerplate at the top, and a CONTROL FLOW tab at the bottom brings structure and sanity. The rest follows naturally.