Data Modeling Part-1 : youtu.be/7UduDWPwGLM?feature=shared
Data Modeling Part-2 : youtu.be/eAI3JwMtJs8?feature=shared
DAX, which stands for Data Analysis Expressions, is a formula language and expression language used in Microsoft Power BI, as well as in other Microsoft products like Power Pivot, Analysis Services, and Power Apps. DAX is primarily used for creating custom calculations and aggregations in Power BI.
Formulas: DAX allows you to create custom formulas to perform calculations on your data. These formulas can range from simple arithmetic operations to complex calculations involving filters and conditions.
Aggregations: DAX is particularly powerful for creating aggregations, such as sums, averages, counts, and more, that can be used in your Power BI reports and visualizations.
Row Context and Filter Context: DAX formulas take into account both row context and filter context. Row context is the current row being evaluated, while filter context is the set of filters applied to the data. DAX expressions adapt to these contexts to return the appropriate results.
Time Intelligence: DAX has built-in functions for handling time-based calculations, making it easier to work with date and time data in Power BI.
Calculated Columns and Measures: In Power BI, you can create calculated columns and measures using DAX. Calculated columns add new columns to your data model, while measures are used for dynamic calculations in visuals, aggregating data as needed.
Relationships: DAX also plays a crucial role in managing relationships between tables in Power BI, which is important for creating meaningful and accurate calculations.
Custom Business Logic: DAX is often used to implement custom business logic, allowing you to create calculations specific to your organization's needs.
Performance Optimization: Optimizing DAX expressions is important for ensuring the performance of your Power BI reports, especially when dealing with large datasets.
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