1. Model (Data Model in Power BI):
- In Power BI: The Model component aligns with the dataset and data model in Power BI, which consists of tables, relationships, calculations (e.g., measures, calculated columns), and aggregations. This is where the data is structured and prepared for analysis.
- Responsibilities:
- Importing and transforming data using Power Query.
- Defining relationships between tables.
- Creating measures using DAX for business logic and calculations.
- Managing data integrity, filtering, and ensuring performance optimisations (e.g., aggregations).
- Can come from multiple sources like SQL databases, APIs, Excel files, etc.
2. View (Report/Visualisation in Power BI):
- In Power BI: The View corresponds to the report pages and visualisations in Power BI. It’s the layer where data is visually represented for users through charts, graphs, tables, KPIs, and slicers.
- Responsibilities:
- Displaying the data model output in a user-friendly way.
- Providing interactivity for the users (e.g., filters, slicers, drill-through).
- Offering different types of visualisations (e.g., bar charts, line graphs, maps) to represent various aspects of the dataset.
- The View in Power BI is tightly linked to the Model, as changes to the Model will immediately reflect in the visuals.
3. Controller (User Interaction in Power BI):
- In Power BI: Power BI doesn’t have a traditional Controller as seen in web or app development, but the Controller concept can be thought of as user interactions with the report, which influence how the data is presented. In this case, the “Controller” role is played by:
- User inputs like slicers, filters, drill-throughs, and page navigation that influence what data is shown in the visualisations.
- DAX formulas and expressions act as dynamic logic controllers, modifying how data is aggregated or calculated based on the user’s actions (e.g., date ranges, selected categories).
- Responsibilities:
- Capturing user selections and sending them to the Model to filter or change data.
- Reacting to filters, slicers, or input parameters in real time to display specific information.
Application of MVC to Power BI Workflow:
- Model (Dataset): You load, transform, and shape your data in Power Query, define relationships, and create calculated fields or measures.
- View (Report Pages): You build the visuals that allow users to see and interact with the data model. Visuals update dynamically based on the underlying data model changes.
- Controller (User Input): Users interact with the report through slicers, filters, drill-downs, or cross-highlighting features. These inputs modify what the report displays, affecting how the data is represented.
Practical Example in Power BI:
- Model: You build a dataset of sales transactions that includes date, product, customer, and sales amount. You create measures like “Total Sales” and “Sales Growth %”.
- View: On your report page, you use a line chart to display total sales over time and a bar chart to show sales by product category.
- Controller: Users interact with the report by filtering by a date range or selecting specific products. The line and bar charts automatically update based on the user’s selection, filtering the data model dynamically.
Benefits in Power BI:
- Separation of Concerns: Power BI lets you separate the data transformation (Model) from visualisation (View). For example, you can modify your data model without affecting the visuals immediately, which simplifies report management.
- Modular Development: Power BI datasets can be reused across multiple reports, allowing different report creators to build views from a common model.
- Scalability and Maintainability: By structuring your Power BI solution with clear models (data) and views (reports), you can maintain a more scalable reporting solution as your datasets grow.
In conclusion, although Power BI doesn’t directly follow the traditional MVC framework, its architecture of separating the data model from visuals and allowing user interactivity does align with the spirit of MVC principles.