A data analyst has been asked to produce a visualization that shows the flow of users through a website. Which of the following is used for visualizing this type of flow?

A Sankey diagram is the most appropriate visualization for showing the flow of users through a website. This type of chart is specifically designed to illustrate the movement of quantities—in this case, users—from one stage to another (e.g., from the homepage to a product page, then to the checkout page). The width of the connecting bands in a Sankey diagram is proportional to the flow quantity, making it easy to identify the most common user paths and drop-off points.

A. Heatmap: Incorrect. A heatmap uses color intensity to show the magnitude of a phenomenon, typically for correlation analysis or to show density on a map, not sequential flow.
B. Choropleth: Incorrect. A choropleth map is used to visualize data across geographical regions by shading them, which is not applicable to website user flow.
C. Word Cloud: Incorrect. A word cloud visualizes the frequency of words in a text dataset and cannot represent a sequential process or flow.
D. Pivot Table: Incorrect. A pivot table is a data summarization tool that aggregates data into a tabular format; it is not a type of visualization for depicting flow.

1. Databricks Documentation

"Sankey diagram": "Sankey diagrams visualize the flow from one set of values to another. They are useful for showing how a specific resource is distributed among multiple destinations or processes." This directly supports its use for visualizing user flow.

Source: Databricks SQL Documentation > Visualizations > Sankey diagram.

2. Databricks Documentation

"Heatmap chart": "Heatmap visualizations use color to show the relationship between two variables... It is most useful for showing the correlation of many variables."

Source: Databricks SQL Documentation > Visualizations > Heatmap chart.

3. Databricks Documentation

"Choropleth map": "Choropleth visualizations are thematic maps that color geographical regions like countries

states

or counties based on aggregate values from your data."

Source: Databricks SQL Documentation > Visualizations > Choropleth map.

4. Databricks Documentation

"Pivot table": "A pivot table aggregates records from a query result into a new tabular display. It’s similar to PIVOT or GROUP BY statements in SQL."

Source: Databricks SQL Documentation > Visualizations > Pivot table.