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flow analysis
designed by Jihwan
Unlock the Power of Data with Dynamic Insights: Dive deep into comprehensive data flow analysis with Microsoft Power BI and Fabric. Experience the magic firsthand by requesting a personalized demonstration of my analysis reports, or explore the "blog" page for inspiration and generate your own unique insights. Let's embark on this data-driven journey together!
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Using Microsoft Fabric Notebooks to Render an SVG Measure From a Semantic Model
In this writing, I’d like to share the small experiment I did recently: querying an SVG measure from a semantic model using a Fabric Notebook , scaling it with Python, and displaying it directly in the notebook output. The idea started from a question from my manager: “If the semantic model produces SVG via DAX, can we render it outside Power BI and reuse it somewhere else?” Once I tried it, new use cases started to appear. The Data Model I Used Below is the sample semantic m
Jihwan Kim
Nov 166 min read


Power BI Semantic Models as Accelerators for AI-Driven Insights
In this writing, I’d like to share how I started learning and experimenting with Copilot and AI-driven features in Power BI and Fabric — and what I discovered about the role of semantic models in making them work effectively. Prerequisites Before jumping into it, here’s the setup I used for experimenting with Copilot and AI-driven insights in Power BI and Fabric. Power BI Service (Fabric workspace) — The experiments were all performed in a Fabric-enabled workspace, since C
Jihwan Kim
Nov 85 min read


Understanding Composite Semantic Models with Direct Lake and Import Tables
In this writing, I’d like to share how I started learning about Composite Semantic Models in Power BI and Fabric — especially now that Direct Lake mode can coexist with Import tables . When this feature first appeared in preview, I was curious how it could help simplify data architecture and improve performance for mixed workloads. After some testing, I’ve learned both the power and the limitations of this hybrid approach. Why This Feature Matters For years, I had to choose
Jihwan Kim
Nov 13 min read


Editing Semantic Models in the Service + TMDL View & Best Practices
In this writing, I’d like to share how I started learning to edit semantic models directly in the Power BI service and how TMDL (Tabular...
Jihwan Kim
Oct 44 min read


Using Semantic Model Refresh Templates & Fabric Data Pipelines for Scalable Model Maintenance
Why I Started Looking at Refresh Templates For years, I’ve learned and worked with refresh schedules in Power BI. At a small scale,...
Jihwan Kim
Sep 213 min read


Why Sunsetting Default Semantic Models is Fabric's Best Decision Yet
In this writing, I’d like to share what I have learned from a recent Microsoft Fabric announcement that, in my opinion, is one of the...
Jihwan Kim
Jul 263 min read


Taming the Beast: From 4-Hour Refreshes to Fast Insights with Power BI and Microsoft Fabric
I. Introduction: The All-Too-Common Incremental Refresh Headache In this writing, I’d like to share how I approach a frustratingly common...
Jihwan Kim
Jul 1313 min read


INFO.DAX + Fabric Notebooks Find Unused Columns
Measure Bloat You Can’t See—But Still Pay For Introduction In this writing, I like to share how I learned to uncover hidden VertiPaq...
Jihwan Kim
May 314 min read


Direct Lake vs. Import vs. Direct Lake + Import: Learn Building Three Storage Patterns That Scale
Introduction In this writing, I like to share how I learned to create three distinct storage patterns for Power BI semantic models—...
Jihwan Kim
May 253 min read


From Zero → Lakehouse: Loading AdventureWorks Sample into Microsoft Fabric (Notebook-Only)
In this writing, I like to share how I learn to populate a clean Fabric Lakehouse —AdventureWorks— using nothing but Spark notebooks. 1...
Jihwan Kim
May 171 min read
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