DAX (Data Analysis Expressions) vs SQL

Developers should learn DAX when working with Microsoft's business intelligence tools like Power BI or Analysis Services to build advanced data models and reports meets developers should learn sql because it is essential for interacting with relational databases, which are foundational in most applications for storing structured data. Here's our take.

DAX (Data Analysis Expressions)

Nice Pick

Developers should learn DAX when working with Microsoft's business intelligence tools like Power BI or Analysis Services to build advanced data models and reports

Pros

• It is essential for creating calculated columns, measures, and tables that support dynamic analytics, such as year-over-year comparisons, running totals, and custom KPIs
• Related to: power-bi, excel-power-pivot

Cons

• Specific tradeoffs depend on your use case

SQL

Developers should learn SQL because it is essential for interacting with relational databases, which are foundational in most applications for storing structured data

Pros

• It is used in scenarios like data analysis, backend development, and business intelligence, enabling efficient data retrieval and management
• Related to: relational-databases, database-management

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use DAX (Data Analysis Expressions) if: You want it is essential for creating calculated columns, measures, and tables that support dynamic analytics, such as year-over-year comparisons, running totals, and custom kpis and can live with specific tradeoffs depend on your use case.

Use SQL if: You prioritize it is used in scenarios like data analysis, backend development, and business intelligence, enabling efficient data retrieval and management over what DAX (Data Analysis Expressions) offers.

The Bottom Line

DAX (Data Analysis Expressions) wins

Developers should learn DAX when working with Microsoft's business intelligence tools like Power BI or Analysis Services to build advanced data models and reports