1. Calculation Level Calculated Columns: Row-level, fixed calculations for each row. Measures: Contextual, on-the-fly calculations. 2. Storage & Performance Calculated Columns: Stores values, consuming more memory. Measures: Lightweight, calculated only when needed. 3. Usage Calculated Columns: Best for permanent fields in tables. Measures: Ideal for dynamic metrics and KPIs. 4. Data Model Impact Calculated Columns: Increases data model size. Measures: No impact on data model size. 5. Example Calculated Columns: Profit = [Sales] - [Cost] (calculated per row). Measures: Total Profit = SUM([Sales]) - SUM([Cost]) (dynamic). 6. Context Dependence Calculated Columns: Independent of filters/visuals. Measures: Adjusts based on filters and visual context. 7. Performance Calculated Columns: Can slow performance due to larger model size. Measures: Optimized for performance, calculated when needed
@@lallusekharan9289 Hi Bro here's the simple rule: Add columns as close to the data source as possible. Power Query (Data Source Side): If you can create the column in Power Query, always do it there. Why? Because it happens once during data refresh, making your report faster. For example, cleaning data, combining columns, or doing some static calculations-Power Query is perfect for that. DAX Calculated Column: Use this only when you need something dynamic that depends on your data model or user interactions, like relationships or filters. But remember, DAX calculations happen in real-time while you're using the report, so too many of these can slow things down. Simple funda: Jo kaam pehle ho sake (Power Query mein), woh wahin karo. Sirf tabhi DAX mein column banao jab uski zarurat ho. This will make your reports faster and easier to manage.
Nice explanation. can you provide all differences in text at video end . Thank you please do more videos
Yeah good suggestions surely will do going forward.for now pls refer below table for difference
1. Calculation Level
Calculated Columns: Row-level, fixed calculations for each row.
Measures: Contextual, on-the-fly calculations.
2. Storage & Performance
Calculated Columns: Stores values, consuming more memory.
Measures: Lightweight, calculated only when needed.
3. Usage
Calculated Columns: Best for permanent fields in tables.
Measures: Ideal for dynamic metrics and KPIs.
4. Data Model Impact
Calculated Columns: Increases data model size.
Measures: No impact on data model size.
5. Example
Calculated Columns: Profit = [Sales] - [Cost] (calculated per row).
Measures: Total Profit = SUM([Sales]) - SUM([Cost]) (dynamic).
6. Context Dependence
Calculated Columns: Independent of filters/visuals.
Measures: Adjusts based on filters and visual context.
7. Performance
Calculated Columns: Can slow performance due to larger model size.
Measures: Optimized for performance, calculated when needed
Can u please explain the different situations when we add columns in both M file and in DAX?
@@lallusekharan9289 Hi Bro
here's the simple rule: Add columns as close to the data source as possible.
Power Query (Data Source Side): If you can create the column in Power Query, always do it there. Why? Because it happens once during data refresh, making your report faster. For example, cleaning data, combining columns, or doing some static calculations-Power Query is perfect for that.
DAX Calculated Column: Use this only when you need something dynamic that depends on your data model or user interactions, like relationships or filters. But remember, DAX calculations happen in real-time while you're using the report, so too many of these can slow things down.
Simple funda: Jo kaam pehle ho sake (Power Query mein), woh wahin karo. Sirf tabhi DAX mein column banao jab uski zarurat ho. This will make your reports faster and easier to manage.