Build Transforms

Transforms handle the data processing logic in your pipelines. They allow you to clean, aggregate, enrich, and shape data using SQL or Python. This section provides step-by-step guides for building different types of Transforms.

Before you begin

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

Transform guides by language

🗃️SQL Transforms

Use SQL for set-based operations, aggregations, filtering, and joins. Perfect for relational data processing.

Start building

🐍Python Transforms

Build flexible transforms with Python for complex logic, text processing, and custom calculations.

Start building

⚡PySpark Transforms

Process large datasets with PySpark for distributed computing and big data transformations.

Start building

❄️Snowpark Transforms

Leverage Snowpark for native Snowflake transformations with Python-like syntax.

Start building

Next steps

After building your 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