Welcome back to the second installment of the DAX Matcha Break. Take a deep breath, clear your workspace, and let’s talk about the „Query Mindset” (DAX for SQL Developers).
As a Power BI Developer, I see one mistake repeated more than any other: treating DAX like a simple calculator. If you want to move from „Junior” to „Architect,” you need to start treating DAX like SQL. In SQL, we build logic in layers. We define a dataset, transform it, and then aggregate it.
Today, we are going to deconstruct a real-world scenario—identifying transaction patterns—using that exact incremental approach.
Table of Contents
1️⃣The Challenge: Which side is winning?
The business wants to know: Which transaction groups are more frequent: incoming (inflow) or outgoing (outflow)?
Our raw data contains various type entries:
- Inflow: 'deposit’, 'transfer’
- Outflow: 'payment’, 'purchase’, 'withdrawal’
We need a clear answer without exporting millions of rows to Excel just to run a Pivot Table. We are going to speak directly to the engine.
2️⃣The SQL „North Star”
In SQL, we naturally think in steps. We create a CASE statement to group the types and then wrap it in a GROUP BY.
SELECT CASE
WHEN type IN ('deposit', 'transfer') THEN 'inflow'
WHEN type IN ('payment', 'purchase', 'withdrawal') THEN 'outflow'
END AS type_group,
COUNT(*) AS cnt_transactions
FROM transactions
GROUP BY type_group
ORDER BY cnt_transactions DESC
LIMIT 13️⃣The DAX Deconstruction: Layer by Layer
Instead of writing a complex measure, we write a DAX Query. This allows us to use variables (VAR) as „virtual tables,” building our answer one step at a time.
Layer 1: The Base Summary
First, we get the raw counts for every transaction type. This is our foundation.
VAR AggregatedByType =
SUMMARIZECOLUMNS (
'transactions'[type],
"RawCount", COUNT ( 'transactions'[id] )
)Layer 2: Adding the Business Logic (The CASE Statement)
Now, we need to map those raw types to our „Inflow” and „Outflow” groups. We use ADDCOLUMNS to extend our virtual table and SWITCH to handle the logic.
VAR MappedGroups =
ADDCOLUMNS (
AggregatedByType,
"type_group",
SWITCH (
'transactions'[type],
"deposit", "inflow",
"transfer", "inflow",
"payment", "outflow",
"purchase", "outflow",
"withdrawal", "outflow",
"unknown"
)
)Layer 3: The Final Roll-up
Finally, we aggregate everything. We group by our new type_group and sum up the counts we calculated in the first step.
RETURN
GROUPBY (
MappedGroups,
[type_group],
"cnt_transactions", SUMX ( CURRENTGROUP(), [RawCount] )
)4️⃣Why This Matters for You
Many analysts spend hours waiting for Excel to process huge datasets because they feel „safer” seeing the rows. But look at what we did here:
- Efficiency: We didn’t pull all the data. We asked the Power BI engine to summarize it before it even reached our view.
- Clarity: By using
VAR, we created a „paper trail” for our logic. If the numbers look wrong, we can checkAggregatedByTypeorMappedGroupsindividually. - Scalability: This logic works just as fast on 10 million rows as it does on 10 thousand.
Tip: Next time you’re facing a complex DAX problem, don’t start with a Measure. Open the DAX Query View or DAX Studio and build it using this table-first approach. Once the table looks right, your Measure will be easy to write.
🎯 The goal of Matcha DAX Break
In conclusion, this is not about memorizing functions.
It’s about:
- Thinking in DAX for SQL Developers
- Practicing without pressure
- Growing even if your current job doesn’t demand it
Short breaks.
Focused exercises.
One sip of matcha at a time 🍵
Moreover, If you’re learning DAX for the future version of your career — this space is for you.