Creating custom visualizations

Transform SQL query results into interactive charts through drag-and-drop operations. The visualization interface converts raw data into visual insights without coding requirements.

Accessing the visualization interface

After executing SQL queries, two tabs appear above your results:

Tab	Purpose
Data	Review query results in table format
Visualization	Create interactive charts

Click Visualization to access the chart builder immediately after query execution.

Interface overview

The visualization workspace consists of three coordinated areas:

Field List (left panel):

• Dimensions - categorical data with document-style icons

• Measures - numerical data with hashtag icons

• Time - temporal fields for trend analysis

Configuration Shelves (center):

• X-Axis/Y-Axis - primary chart structure

• Rows/Columns - multi-panel comparisons

• Filters - data subsetting

• Color/Size/Opacity - visual encoding

Canvas (right):

• Real-time chart display with immediate updates

• Interactive zoom, pan, and exploration capabilities

Essential controls

Expand the section below to see key toolbar buttons that accelerate chart creation.

See main cotrols description

Control	Function	Usage
Aggregation	Raw vs. aggregated data display	Turn OFF to see individual records; keep ON to see totals/averages
Mark Types	Chart type selection (Bar, Line, etc.)	Switch between bar charts, line graphs, scatter plots based on your analysis needs
Stack Mode	Create a Stack chart or Normalize a chart	Stack bars to show totals; normalize to compare percentages across categories
Transpose	Switch the x-axis and y-axis of the chart	Flip chart orientation when categories are hard to read or compare
Sort Order	Sort in Ascending or Descending Order	Arrange data from highest to lowest values (or vice versa) to identify top performers
Axis Resizing	Resize the axes	Zoom into specific value ranges to examine data in more detail
Layout Mode	Resize the chart or use the auto-sized chart	Switch to manual sizing when you need larger charts for presentations
Exploration Mode	Explore data. You can choose either point mode or brush mode	Select individual data points or drag to select multiple points for detailed analysis
Export	Save visualizations (PNG or SVG)	Download chart images for reports, presentations, or documentation
Export as CSV	Export visualized data in CSV format	Download the underlying data to analyze in Excel or other spreadsheet programs
Export code	Export visualization as code in Python or JSON (Graphic Walker)	Get code to recreate this exact chart in your own Python scripts

Building visualizations: sample workflow

Example scenario: Analyzing operational data with fields like category_field, performance_metric, timestamp, region.

1

Create foundation chart

• Drag categorical field (e.g., category_field) → X-Axis

• Drag numerical field (e.g., performance_metric) → Y-Axis

• Interface creates aggregated bar chart automatically

2

Refine visualization type

Click Mark Types button for alternatives:

• Bar charts: category comparisons

• Line charts: trend analysis over time

• Scatter plots: correlation analysis (turn off aggregation)

3

Add analytical depth

• Drag additional categorical field → Color shelf = visual differentiation

• Drag numerical field → Size shelf = proportional representation

• Multiple dimensions reveal hidden relationships

[SCREENSHOT: Progressive chart building showing basic bar chart evolving into multi-dimensional analysis]

Advanced techniques

Multi-panel analysis

Create comparative views using Rows/Columns shelves:

• Drag categorical field → Rows = separate panels for each category

• Consistent scales enable direct cross-comparisons

• Reveals patterns obscured in aggregated views

Filtering and focus

Use Filters shelf for targeted analysis:

• Drag categorical fields for subset selection

• Drag numerical fields for range-based filtering

• Multiple filters combine for precise data exploration

Interactive exploration

• Manual chart resizing using Resize button options

• Zoom and pan capabilities for detailed examination

• Export capabilities for sharing and integration

Performance tip: Apply filters before complex visualizations to maintain responsiveness with large datasets.

Evaluating your results

After creating custom visualizations, assess whether this approach meets your analytical needs:

Visualization complexity: Can you create the charts and insights required for your operational decisions?

Data exploration depth: Do the interactive capabilities provide sufficient analytical flexibility?

Sharing and integration: Do export options support your reporting and collaboration workflows?

Performance and scalability: Does the interface handle your data volume and complexity requirements effectively?

Next steps

Based on your custom visualization experience, you may need more advanced capabilities. For comprehensive guidance on selecting appropriate business intelligence tools that match your specific requirements and organizational needs, see Selecting BI tools.