Build Snowpark Transforms

Snowpark Transforms leverage Snowflake's native compute engine to process data directly within Snowflake using Python-like syntax. They provide the power and familiarity of Python while taking advantage of Snowflake's performance, scalability, and security features.

Before you begin

• Ensure you have an Ascend Project and Workspace
• Have Read Components or other data sources ready as inputs
• Snowflake as your Data Plane

Snowpark Transform guides

🔄Simple Snowpark Transforms

Full refresh transformations using Snowpark DataFrames. Perfect for leveraging Snowflake's native compute with Python syntax.

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📈Incremental Snowpark Transforms

Process only new or changed data with Snowpark. Ideal for efficient processing within Snowflake's optimized environment.

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🧠Smart Snowpark Transforms

Intelligent partition-based processing with Snowpark. Perfect for large datasets with Snowflake's automatic optimization.

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Next steps

After building your Snowpark Transforms:

• 📤 Write transformed data to destinations
• 🧪 Add data tests to validate transformation logic
• 📋 Create Task Components for additional processing
• 🔄 Set up Automations to orchestrate your pipeline