{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Databricks Feature Engineering client for creating, managing, and serving feature tables within Databricks workspaces, including training models on feature data and publishing to online stores.","skillfed_tags":["feature-store","databricks-native","ml-pipeline"],"use_cases":["Build and manage feature tables in Databricks for ML model training pipelines.","Publish computed features to online stores for low-latency real-time inference.","Migrate workspace-level feature tables to Unity Catalog for governance and access control.","Integrate feature engineering workflows with MLflow experiment tracking in Databricks.","Unit test feature engineering logic locally before deploying to Databricks clusters."],"what_it_does":"The Databricks Feature Engineering client is a Python library for managing feature tables within the Databricks ecosystem. It provides APIs to create, read, and write feature tables, train models using feature data, and publish tables to online stores for real-time serving. The library also supports upgrading workspace feature table metadata to Unity Catalog.\n\nThis is a Databricks-native tool that runs on Databricks runtimes and integrates with the broader Databricks ML platform. It depends on mlflow-skinny for experiment tracking, boto3 and azure-cosmos for cloud storage backends, databricks-sdk for platform communication, and standard ML libraries like numpy and protobuf. The library is actively maintained and has no known security vulnerabilities.","worth_installing":"Yes, if you are working within Databricks and need to manage feature tables or integrate feature engineering into ML workflows. The library is actively maintained, has low install friction, and integrates with standard Databricks ML tools. However, the proprietary license restricts use to Databricks Platform Services; confirm your Databricks agreement permits this library before production deployment."},"id":"databricks-feature-engineering","links":{"html":"https://skillfed.io/packages/databricks-feature-engineering","md":"https://skillfed.io/packages/databricks-feature-engineering.md","pypi":"https://pypi.org/project/databricks-feature-engineering/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-20","license_spdx":null,"license_treatment":"unclear","name":"databricks-feature-engineering","python_support":"supports_current","summary":"Databricks Feature Engineering Client"},"popularity":{"monthly_downloads":3528330,"position":2591,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.16.1"}