Snowflake Notebooks provide an interactive environment where developers and analysts can combineSQL,Python, visualization libraries, and documentation. Use cases include exploratory analysis, ETL prototyping, data engineering workflows, machine learning model development, and dashboard creation using Streamlit components.

Notebooks allow mixing of SQL and Python cells with shared session state, enabling smooth transitions between Snowpark DataFrames, SQL queries, visual charts, and markdown explanations. They enhance reproducibility and collaboration, supporting versioning, parameterization, and seamless Snowflake compute integration.

Incorrect options:

Raw data is stored in Snowflake tables, not in notebooks.

User authentication is handled by the Cloud Services Layer.

Managing cloud storage is automatic and not a notebook responsibility.

Thus, Snowflake notebooks are an end-to-end development and analytics interface.

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