databricks-feature-engineering
Databricks Feature Engineering Client
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8.10 and can only run on Databricks runtimes; local/CI use is limited to unit testing.
- Low install friction with a pure-Python wheel.
- Active maintenance with a release 25 days ago.
License · maintenance · safety
Databricks Proprietary License (unclear) — Licensed under Databricks Proprietary License with unclear treatment. Use is restricted to connection with Databricks Platform Services under a separate agreement; the license terminates if that agreement ends or is breached. Not open-source; review Databricks terms before deployment.
last release 2026-07-20 (25 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,528,330 downloads/mo, #2,591 on PyPI
Alternatives
Verify before relying
pip install databricks-feature-engineering
from databricks.feature_engineering import FeatureEngineeringClient
client = FeatureEngineeringClient()- Specific feature table schema requirements and supported data types.
- Online store provider support and configuration details beyond AWS/Azure/GCP.
- Performance characteristics for large-scale feature table operations.
- Backward compatibility guarantees across minor versions.
What it is and 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.
This 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
databricks-feature-engineering on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with a release 25 days ago. Depends on mlflow-skinny, boto3, azure-cosmos, databricks-sdk, and other common libraries, all standard in ML environments.
Requires Python >=3.8.10 and can only run on Databricks runtimes; local/CI use is limited to unit testing.
License in practice
Licensed under Databricks Proprietary License with unclear treatment. Use is restricted to connection with Databricks Platform Services under a separate agreement; the license terminates if that agreement ends or is breached. Not open-source; review Databricks terms before deployment.
Quickstart
pip install databricks-feature-engineering
from databricks.feature_engineering import FeatureEngineeringClient
client = FeatureEngineeringClient()Verify before relying
- Specific feature table schema requirements and supported data types.
- Online store provider support and configuration details beyond AWS/Azure/GCP.
- Performance characteristics for large-scale feature table operations.
- Backward compatibility guarantees across minor versions.
Package facts
| License | Databricks Proprietary License unclear |
| Python support | Supports the current Python release >=3.8.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesmlflow-skinnypyyamlboto3dbl-tempoazure-cosmosnumpyprotobufsqlparsedatabricks-sdk |
| Maintenance | Actively maintained 25 days since the last release |
| First released | |
| Downloads | 3,528,330 / month, #2,591 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 |
Evidence: databricks_feature_engineering-0.16.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “feature table management”
- databricks-feature-engineeringDatabricks Feature Engineering client for creating, managing, and…
- azureml-featurestoreProvides Python SDK access to Azure ML Feature Store for developing…
- PTablePTable formats tabular data into visually appealing ASCII tables,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also databricks-agents · databricks-feature-store · databricks-ai-search · databricks-dlt · dbt-databricks · tecton · unitycatalog-ai · unitycatalog-openai · dbldatagen · databricks-connect