However, you can achieve 3D visualizations using Databricks in a couple of ways:

1. Export and Visualize:

• Export your processed data from Databricks (e.g., as a CSV file).
• Use external 3D visualization tools like Plotly, Matplotlib (with mplot3d), or specialized libraries like Mayavi to create and display your 3D plots.

1. Integrate with 3D Visualization Platforms:

• Connect Databricks to platforms like Power BI or Tableau, which have robust 3D visualization capabilities.
• Push your Databricks data into these platforms and leverage their tools to build interactive 3D visuals.

Example: 3D Scatter Plot using Plotly (Python)

Assuming you have your data in a Databricks DataFrame, you can follow these steps:

1. Export Data: Convert your DataFrame to a Pandas DataFrame and export it as a CSV.

Python

import pandas as pd

# Assuming ‘df‘ is your Databricks DataFrame

pandas_df = df.toPandas()

pandas_df.to_csv(“data.csv”, index=False)

1. Create 3D Plot using Plotly:

Python

import plotly.express as px

df = pd.read_csv(“data.csv”)

fig = px.scatter_3d(df, x=’x_column’, y=’y_column’, z=’z_column’)

fig.show()

Additional Notes:

• You might need to install required libraries (like Plotly) within your Databricks environment.
• The choice of method depends on your specific requirements, data size, and preferred tools.