Snowflake Tools

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Overview

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Tool Name

snowflake_tools

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Purpose

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Key Features & Functions

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Input Parameters for Each Function

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_list_stage_contents

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_add_file_to_stage

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_delete_file_from_stage

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_read_file_from_stage

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_cortex_search

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_run_snowpark_python

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Use Cases

1. Create data visualizations from Snowflake data with matplotlib or similar libraries.
2. Perform advanced statistical analysis on enterprise datasets.
3. Build and run machine learning models inside Snowflake.
4. Process CSVs by uploading to stages and analyzing with Snowpark.
5. Generate automated reports and dashboards with Python-based workflows.
6. Search indexed documentation and knowledge bases using Cortex AI.
7. Spin up temporary data workflows that leverage Snowflake compute.
8. Develop custom transformation scripts for large-scale datasets.
9. Combine SQL and Python for interactive analysis.
10. Manage file-based pipelines for ETL and data quality validation.

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Workflow/How It Works

1. Land files with _add_file_to_stage or verify with _list_stage_contents.
2. Transform and analyze using _run_snowpark_python with access to Snowflake data.
3. Discover content via _cortex_search for AI-ranked retrieval when needed.
4. Retrieve or share with _read_file_from_stage, optionally saving results as artifacts.

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Integration Relevance

• data_connector_tools for SQL-centric operations that complement Snowpark workloads.
• file_manager_tools to register outputs and maintain lineage.
• artifact_manager_tools to persist visualizations, CSVs, or notebooks.
• project_manager_tools to track Snowflake tasks across missions and todos.
• image_tools to inspect or analyze images generated by Snowpark code.

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Configuration Details

• Use forward slashes in stage paths and avoid special characters.
• Ensure your role has USAGE on the database and schema, plus required privileges on the stage.
• In Snowpark, set a top-level result variable in your code to capture returned output.
• Cortex services must be provisioned and indexed before you can query them.
• Package names should exclude standard libraries and list only additional needs.

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Limitations or Notes

1. Stage and Snowpark operations require appropriate Snowflake permissions.
2. Large files and long-running Python jobs may hit resource or timeout limits.
3. First-time package installation in Snowpark can add latency.
4. Cortex search availability depends on account features and service configuration.
5. Complex Python tasks can require significant compute and time.
6. Binary file handling may require is_binary = true and max_bytes tuning.
7. Network connectivity to Snowflake is required for all operations.

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Output

• Stage Operations Listings, upload confirmations, deletions, and resolved file paths.
• File Content Returned text or binary bytes with size and encoding info when requested.
• Cortex Search Ranked results with relevance scores and snippets.
• Snowpark Execution Captured result, console logs, and optional artifacts for downstream use.
• Artifacts Artifact ids and links for reports, images, or data files created during execution.
• Errors Clear messages for permission issues, invalid paths, missing services, or runtime failures.