Python SDK Setup Guide

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.

This guide walks you through installing and configuring the Elementary Python SDK to report assets, test results, and execution metadata from your Python pipelines.

​

Installation

The Python SDK is installed via pip. Installation instructions and package requirements will be provided when you get access to the beta.

​

Configuration

The Python SDK requires configuration to connect to your Elementary Cloud environment. You’ll need:

1. Elementary Cloud API Key - Get this from your Elementary Cloud account settings
2. Environment ID - Your Elementary environment identifier

Configuration can be done via environment variables or a configuration file, similar to how the Elementary CLI is configured.

​

Basic Concepts

The SDK is designed to work with any Python testing framework. The core functionality includes:

• Reporting assets - Tables, files, vector stores, or any data entity produced by your Python pipeline
• Reporting test results - Results from any testing framework (Great Expectations, pytest, custom frameworks, etc.)
• Tracking execution metadata - Pipeline runs, timing, status, and errors
• Reporting lineage - Connecting Python assets to upstream and downstream dependencies

​

Integration with Existing Frameworks

The SDK works with any Python testing framework. You can wrap your existing tests to report results to Elementary without changing your test logic:

• Great Expectations - Report GE validation results
• Pytest - Report pytest test outcomes
• Custom frameworks - Report results from any homegrown testing solution
• DQX and other tools - The SDK is framework-agnostic

​

Reporting Execution Metadata

Track pipeline execution details including:

• Pipeline name and environment
• Start and end times
• Duration
• Success/failure status
• Error messages and stack traces

​

Reporting Lineage

Connect your Python assets to upstream and downstream dependencies, creating a complete lineage graph that includes:

• Python pipeline outputs
• dbt models
• Warehouse tables
• Unstructured data sources
• Vector stores
• ML model outputs

​

Next Steps

• See Usage Examples for conceptual examples
• Learn about the SDK Overview

​

Need Help?

If you need assistance with setup or have questions about the SDK, reach out to the team.