Next Stop: Insights! How Streamlit and Snowflake Power Up Data Stories

Streamlit enables building data apps with Python code only, requiring no frontend experience

Key benefits of Streamlit + Snowflake integration:

• Direct access to data stored in Snowflake
• Single platform for data storage, analysis and visualization
• Immediate reflection of data changes in apps
• Easy sharing and collaboration through role-based access control
• Fully managed compute/storage environment

The Python editor in Snowflake’s web UI allows:

• Interactive Streamlit app development
• Package management via Anaconda channel
• Live preview of changes
• SQL query integration with Snowpark/Pandas

Current limitations:

• Feature is in public preview
• No native version control or CI/CD pipeline
• Query size limited to 32MB
• Limited debugging capabilities
• Some packages require external activation

Best practices:

• Start with small warehouse size and scale as needed
• Consider performance tradeoffs between Snowpark vs Pandas for data manipulation
• Ensure business value justifies development costs
• Can be used for both POC and production with proper CI/CD
• Structure apps with clear goals and visualizations

Security handled through Snowflake’s role-based access control with separate permissions for:

• App development
• App viewing
• Underlying data access