{"categories":[{"label":"Front-Ends","url":"https://skillfed.io/packages/category/database-front-ends"}],"enrichment":{"capability":"teradataml provides Python access to analytic functions running on Teradata Vantage, enabling data analysis, machine learning, and data transformation without writing SQL.","skillfed_tags":["in-database-analytics","automl","feature-store"],"use_cases":["Build and deploy machine learning models (classification, regression, clustering) directly on Teradata without exporting data.","Perform feature engineering and feature store management for ML pipelines using Teradata as the compute backend.","Run AutoML workflows to automatically select algorithms, tune hyperparameters, and generate SHAP explanations on large datasets.","Transform and filter data at scale using DataFrame operations and array functions without SQL coding.","Integrate Teradata analytics into Python data science workflows alongside pandas and scikit-learn."],"what_it_does":"teradataml is a Python interface to Teradata Vantage's in-database analytics engine. It lets you perform machine learning, data transformation, and statistical analysis directly on Teradata without writing SQL\u2014operations execute server-side on Vantage and results return to Python. The library includes AutoML functions (clustering, classification, regression, fraud detection, churn prediction), a feature store for managing ML features, data manipulation via DataFrame-like objects, and integration with pandas and other open-source libraries.\n\nThe package depends on teradatasql and teradatasqlalchemy for database connectivity, plus standard Python libraries like SQLAlchemy, pandas, requests, and cryptography. It supports OAuth 2.0 authentication (including Device Code Grant for PING and KEYCLOAK IDPs) and optional extras for visualization and EDA. Recent releases added array operations, cross-validation support in hyperparameter tuning, and FilterManager for feature processing workflows.","worth_installing":"Yes, if you have a Teradata Vantage instance and need Python-native analytics without SQL. The package is actively maintained, has low install friction, and offers a broad feature set (AutoML, feature store, data manipulation). However, verify the Teradata License Agreement terms for your use case first, and confirm that optional dependencies (scikit-learn, lightgbm) are available if you need AutoML or visualization features."},"id":"teradataml","links":{"html":"https://skillfed.io/packages/teradataml","md":"https://skillfed.io/packages/teradataml.md","pypi":"https://pypi.org/project/teradataml/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-12","license_spdx":null,"license_treatment":"unclear","name":"teradataml","python_support":"supports_current","summary":"Teradata Vantage Python package for Advanced Analytics"},"popularity":{"monthly_downloads":485500,"position":6398,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"20.0.0.11"}