Power BI External Tools I Can’t Live Without (And Why You Probably Shouldn’t Either)
If you’re building anything at all involved in Power BI and you’re not using external tools yet, you’re making your workflow harder than it needs to be.
This post kicks off a short series on Power BI external tools — tools that plug directly into Power BI Desktop and give you far more control over your semantic models, DAX, performance, and deployments. Everything here comes from real, day-to-day usage, not theory or feature checklists.
Below are the five external tools I use constantly, followed by two honorable mentions that round out a modern Power BI workflow.
What Are Power BI External Tools (Really)?
External tools are third-party applications that connect directly to your Power BI semantic model. Once installed, they show up in the External Tools ribbon in Power BI Desktop and open with full context of whatever model you’re working on.
Practically speaking, they let you:
Do things Power BI Desktop doesn’t expose
Work faster at scale
Treat your semantic model like an engineered artifact, not a UI object
If Power BI Desktop is the cockpit, external tools are the engine room.
The 5 Power BI External Tools I Use Every Day
1. Tabular Editor
The backbone of serious semantic model development
If I had to uninstall every tool except one, this is the one I’d keep.
I spend more time in Tabular Editor than I do in Power BI Desktop or the Service when working on semantic models. It’s where real modeling work happens.
Key reasons it’s indispensable:
Calculation Groups
This alone is a game changer. Time intelligence, KPI logic, currency conversion — all reusable, all scalable, without measure explosion.Bulk metadata management
Rename, re-folder, format, and document hundreds of measures in seconds.Advanced automation
C# scripting lets you automate repetitive modeling tasks and enforce standards.Best practice analyzers & DAX tooling
Guardrails that help keep models clean as they grow.
If you’re building enterprise or shared models and not using Tabular Editor, you’re leaving both quality and velocity on the table.
2. Measure Killer
The confidence tool nobody talks about
Measure Killer is almost always open on my machine. It’s the only tool I use that truly connects the semantic model to the report layer.
What makes it so valuable:
Shows exactly which tables, columns, and measures are actually used
Tells you where they’re used — down to the report page
Identifies downstream dependencies across reports
Highlights cleanup opportunities to reduce model size
But the real value is this:
Measure Killer removes fear.
When you maintain shared or long-lived models, deleting anything feels risky. Measure Killer gives you the confidence to clean up safely — which is huge for governance and performance.
3. DAX Studio
Still relevant, even with DAX Query View
I’ll be honest — I use DAX Studio less now that DAX Query View exists in Power BI Desktop and the Service. But it’s still very much part of my toolkit.
Where it still shines:
Server timings and query plan analysis
Understanding why visuals are slow — storage engine vs formula engine — is still best done here.Query capture from real visuals
You’re debugging what Power BI actually runs, not hypothetical DAX.DMVs and VertiPaq Analyzer
Deep insight into model memory, compression, and structure.
If performance matters, DAX Studio still earns its place.
4. Bravo
Small tool, sneaky powerful
Bravo doesn’t get enough credit. It’s built by the SQLBI team, which already tells you it’s opinionated in the right ways.
How I use it most:
Mass DAX formatting
Clean, consistent DAX across the entire model in one shot.Time intelligence automation
Helpful for bootstrapping or standardizing patterns.Model health checks
Lightweight best-practice rules that catch issues early.
One underrated feature I love:
You can export data directly from model tables without writing DAX or building visuals — even for live connections or DirectQuery models.
It’s a great productivity and quality-of-life tool.
5. ALM Toolkit
Version control without the headache
ALM Toolkit is my go-to for comparing and promoting semantic models.
Typical use cases:
Compare two local model files
Compare a local model to one deployed in the Service
See exactly what changed — measures, tables, relationships, metadata
Selectively merge or deploy updates
Two enterprise scenarios where it really shines:
Dev → Test → Prod promotion
Schema drift detection (catching unintended changes before they become problems)
If you’ve ever wished Power BI had better built-in deployment tooling, ALM Toolkit fills that gap nicely.
Honorable Mentions (Not Power BI-Specific, Still Essential)
SQL Server Management Studio (SSMS)
SSMS lets you connect directly to Power BI workspaces via Analysis Services, which unlocks a lot of operational power:
Inspect all semantic models in a workspace
Run DMV queries against live models
Manage incremental refresh partitions
Refresh specific partitions when historical data changes
For large organizations with centralized models, SSMS becomes a diagnostic and ops tool — not just an admin UI.
Visual Studio Code (VS Code)
VS Code becomes especially powerful when paired with Power BI Project (PBIP) file formats.
Why it matters:
Models and reports are broken into readable JSON and text files
Git diffs show exactly what changed
Enables PR-based review of semantic model updates
Supports automation and refactoring at scale
This is where Power BI development starts to feel like real software engineering, not just report editing.
Final Thoughts
The big shift to understand here is this:
Power BI Desktop is no longer the center of serious development.
The semantic model is the product, and external tools are how you engineer it properly.
If you’re brand new to external tools, buckle up — once you start using them, it’s hard to imagine going back.
In the next posts and videos, I’ll break down each of these tools in more detail with real examples and workflows. If you’re ready to level up how you build in Power BI, you’re in the right place.