sagemaker-studio

What it is and what it does

SageMaker Studio is an open-source Python SDK that wraps Amazon SageMaker Unified Studio's resource model, letting you programmatically access domains, projects, connections, and data catalogs without writing boilerplate AWS API calls. It abstracts credential management, region configuration, and common patterns for querying project metadata and S3 paths.

The library is designed primarily for use within SageMaker Unified Studio's JupyterLab environment, where it auto-detects credentials, but can also be used standalone by supplying AWS credentials via profile or boto3 Session. It includes utilities for SQL execution, DataFrame operations, and both local and remote execution APIs, plus connectors to multiple databases (MySQL, PostgreSQL, Snowflake, BigQuery, DynamoDB, Vertica, Trino, Oracle) via SQLAlchemy and specialized drivers.

Use it for:

• Query project metadata (IAM role, KMS key, MLflow server ARN) and S3 paths from within a SageMaker notebook without manual boto3 calls.
• Access and list databases and tables defined in a project's data catalog for data exploration and ETL workflows.
• Retrieve and manage project connections and secrets for multi-database analytics pipelines.
• Execute SQL queries and DataFrame operations against project-connected data sources using unified utility methods.
• Automate resource discovery and configuration in SageMaker Unified Studio environments for CI/CD or batch processing.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

A Python SDK for accessing Amazon SageMaker Unified Studio resources—domains, projects, connections, databases, and tables—through a unified interface with minimal code.

Yes, if you work within Amazon SageMaker Unified Studio and need programmatic access to project resources. The library is actively maintained, has no known vulnerabilities, and low install friction. The large dependency tree (30 packages) is justified by support for multiple database backends; if you only use a subset of connectors, you may want to verify which are actually required. Outside SageMaker Unified Studio, it requires explicit credential setup but remains a clean abstraction over raw AWS API calls.

Install

sagemaker-studio on PyPI

pip install sagemaker-studio

uv add sagemaker-studio

poetry add sagemaker-studio

Installing sagemaker-studio

Before you install

Low install friction; pure Python wheel with no compiled dependencies. Active maintenance as of 2026-08-13. Requires Python 3.10 or newer. Depends on 30 runtime packages including boto3, pandas, sqlalchemy, and cloud-specific connectors (Snowflake, BigQuery, DynamoDB, Vertica, Trino, Oracle), which are all standard and widely maintained.

License in practice

Licensed under Apache License 2.0 (permissive). No restrictions on commercial or private use; you may modify and distribute under the same license terms.

Quickstart

pip install sagemaker-studio

from sagemaker_studio import ClientConfig, Project

conf = ClientConfig(region="us-east-1")
proj = Project(config=conf)
print(proj.name, proj.iam_role)

Requires Python 3.10 or newer. AWS credentials must be available via environment (automatic in SageMaker Unified Studio JupyterLab), AWS named profile, or explicit boto3 Session.

Verify before relying

• Whether the library's 30 runtime dependencies are all required for basic use or only for specific features (e.g., database connectors).
• Performance and scalability characteristics when working with large projects or many concurrent connections.
• Whether local and remote execution APIs (mentioned in TOC) are fully implemented in version 1.1.30.

Package facts

Evidence: sagemaker_studio-1.1.30-py3-none-any.whl

Keywords: AWS, Amazon, SageMaker, SageMaker Unified Studio, SDK

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