{"categories":[{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks"}],"enrichment":{"capability":"A Python SDK for accessing Amazon SageMaker Unified Studio resources\u2014domains, projects, connections, databases, and tables\u2014through a unified interface with minimal code.","skillfed_tags":["aws-sdk","sagemaker","data-catalog"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"sagemaker-studio","links":{"html":"https://skillfed.io/packages/sagemaker-studio","md":"https://skillfed.io/packages/sagemaker-studio.md","pypi":"https://pypi.org/project/sagemaker-studio/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-studio","python_support":"supports_current","summary":"Python library to interact with Amazon SageMaker Unified Studio"},"popularity":{"monthly_downloads":19258461,"position":1071,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.1.30"}