> ## Documentation Index
> Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Python SDK Usage Examples

This page provides conceptual examples of how the Elementary Python SDK can be used in different scenarios.

## Reporting Assets

### Tables and Views

Report tables or views created by your Python pipeline. Include metadata like schema, database, and description to make them discoverable in the Elementary catalog.

### Files and Unstructured Data

Report files, blobs, or unstructured data stored in object storage (S3, GCS, Azure Blob, etc.). Include location, format, and other relevant metadata.

### Vector Stores

Report vector stores used in AI/ML pipelines. Include information about the store type (Pinecone, Weaviate, etc.), index names, and dimensions.

## Reporting Test Results

### Basic Test Results

Report simple test outcomes - whether a test passed or failed, along with the test name and type.

### Detailed Test Results

Report comprehensive test information including:

* Test name and type
* Pass/fail status
* Actual vs expected values
* Column-level details
* Failed row counts
* Sample data from failed rows

### Framework Integration

Report test results from any framework:

* Wrap Great Expectations validations
* Report pytest outcomes
* Capture results from custom test frameworks
* Integrate with DQX or other data quality tools

## Complete Pipeline Example

A typical Python pipeline using the SDK would:

1. **Start tracking** - Begin a pipeline run with metadata (name, environment)
2. **Report input assets** - Document what data sources the pipeline consumes
3. **Execute transformations** - Run your existing Python code
4. **Report output assets** - Document what the pipeline produces
5. **Run and report tests** - Execute data quality checks and report results
6. **End tracking** - Complete the run with success/failure status and timing

This creates a complete observability record in Elementary, unified with your dbt and cloud tests.

## Integration with Orchestrators

The SDK can be integrated with any orchestrator:

* **Airflow** - Wrap your Python tasks to report execution and test results
* **Prefect** - Use the SDK in Prefect flows and tasks
* **Dagster** - Report assets and tests from Dagster ops
* **Custom orchestrators** - Works with any Python-based orchestration system

## ML Pipeline Example

For ML pipelines, you can:

* Report training data assets
* Report model artifacts
* Report test/validation datasets
* Report model performance metrics as test results
* Track model training runs
* Connect models to their training data and downstream consumers

This provides full observability for ML workflows alongside your data engineering pipelines.

## Next Steps

* Review the [Setup Guide](/cloud/python-sdk/setup-guide) for installation and configuration
* Learn about the [SDK Overview](/cloud/python-sdk/overview)