Build Snowpark Transform Components
Snowpark Transform Components 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.
📈 Incremental Snowpark Transforms
Process only new or changed data with Snowpark. Ideal for efficient processing within Snowflake's optimized environment.
🧠 Smart Snowpark Transforms
Intelligent partition-based processing with Snowpark. Perfect for large datasets with Snowflake's automatic optimization.
Next steps
After building your Snowpark Transform Components:
- 📤 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