When you need to analyze data from multiple tables, use data modeling to join them into a single model. This topic describes the data modeling workflow.
Prerequisites
You have created a dataset. For more information, see Create a dataset.
Create a relational model
The Quick BI relational model is a data modeling framework for joining and analyzing data from multiple tables. It solves common physical modeling issues, making it ideal for multidimensional data analysis in complex business scenarios.
Follow these steps:
On the dataset creation page, double-click or drag a table to the canvas. Alternatively, you can use custom SQL to create a table.
Drag another table onto the canvas and configure the logical relationship. For detailed instructions, see Relational model operations.
Click OK to create the relationship.
You can repeat these steps to join multiple logical tables.
Create a physical model
If you want to build a physical model, do not build the model directly on the relational model canvas. After you drag the first table to the canvas, click the 
icon on the right side of the logical table and select Go to Physical Canvas, or double-click the logical table to go to the physical canvas. There, you can configure multi-table joins or unions.
Follow these steps:
Click the
icon on the right side of the logical table and select Go to Physical Canvas, or double-click the logical table to go to the physical canvas.
After you drag a second table onto the canvas, you can join or union the tables.
To return to the relational model canvas, click the dataset name.
Additional modeling capabilities
Placeholder
Placeholders let you dynamically pass parameters to SQL, calculated fields, and charts. They support multiple types, including value, expression, tag, system, condition, and acceleration. Combined with query controls, they enable interactive analysis such as data filtering, metric switching, and reference line adjustments. Acceleration placeholders also improve extraction performance for large datasets and increase report flexibility and responsiveness. For more information, see Placeholder.
HINT statement
For large data volumes or complex queries, you can configure a HINT statement in your dataset to ensure efficient data retrieval. A correctly used HINT statement improves query performance, reduces response times, and optimizes resource use. For more information, see HINT statement.
Next steps
After building the model, click Done, Start Data Processing to open the data processing interface. For more information, see Data processing.
