> ## Documentation Index
> Fetch the complete documentation index at: https://docs.corvic.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Data Enrichment with Code-Based Augmentation
> Learn how to enrich your data using code-based augmentation techniques in Corvic AI
## Data Enrichment with Code-Based Augmentation
This tutorial demonstrates how to enrich your data using code-based augmentation techniques in Corvic AI. Learn how to enhance your datasets with custom code transformations and augmentations.
## What You'll Learn
* Creating custom data transformations using code-based augmentation
* Implementing augmentation pipelines to enhance datasets
* Best practices for data enrichment workflows
## Key Concepts
### Code-Based Augmentation
Code-based augmentation allows you to transform data using custom code, add computed fields and features, enrich datasets with external data, and create reusable augmentation logic for complex transformations.
### Use Cases
Common use cases include **feature engineering** (creating new features from existing data), **data cleaning** (applying custom cleaning logic), **data transformation** (transforming formats and structures), **external enrichment** (combining data from multiple sources), and **computed fields** (generating derived fields using calculations).
## Implementation Steps
### Step 1: Define Your Augmentation Logic
Create custom code to define how you want to enrich your data:
```python theme={null}
def enrich_data(row):
# Your custom augmentation logic
row['enriched_field'] = compute_value(row)
return row
```
### Step 2: Apply Augmentation
Apply your augmentation code to your dataset by configuring augmentation settings, specifying input and output fields, and setting up transformation parameters.
### Step 3: Validate Results
Review enriched data, validate transformations, check data quality, and monitor performance.
## Best Practices
Write reusable, modular augmentation functions with proper error handling. Optimize code for large datasets and test augmentation logic on sample data first. Document your augmentation logic clearly for maintainability.
## Related Documentation
Learn about uploading and managing data sources.
Create Corvic Tables from your enriched data for distributed processing.
Generate embedding spaces from enriched Corvic Tables.
Learn about Python integration for advanced workflows.
## Additional Resources
* [Watch on YouTube](https://www.youtube.com/watch?v=gC8xz9Vf6lA\&list=PLg5ygoWwy28IOi8xYqjr_HlSBJorIOEvu\&index=3)
* [Full Playlist](https://www.youtube.com/playlist?list=PLg5ygoWwy28IOi8xYqjr_HlSBJorIOEvu)