orion-py-client
Python Client for Orion Feature Store to push/produce Model Features and get features' metadata
Decision gist · record as of 2026-08-14
Yes, if you are already running Spark-based feature engineering and have Orion Feature Store deployed as your backend. The low install friction and permissive license make it a straightforward addition. However, the aging maintenance status (343 days since last release) means you should verify that it remains compatible with your Spark, Kafka, and Orion versions before committing to production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.7+; Kafka feature push functionality requires Apache Spark 3.0+ and spark-sql-kafka.
- Low install friction with only 2 runtime dependencies (cloudpathlib and protobuf).
- Maintenance status is aging—last release was 343 days ago—so expect slower response to issues or updates.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), so you can use, modify, and distribute this package freely in commercial or private projects without copyleft obligations.
last release 2025-09-05 (343 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 225,969 downloads/mo, #9,209 on PyPI
Alternatives
Verify before relying
pip install orion-py-client==0.1.14
from orion_py_client import OrionPyClient
client = OrionPyClient(
features_metadata_source_url="your_features_metadata_source_url",
job_id="your_job_id",
job_token="your_job_token"
)
offline_src_type_columns, offline_col_to_default_values_map, entity_column_names = client.get_features_details()- Whether the package actively maintains compatibility with recent versions of Apache Spark and Kafka ecosystems.
- Whether cloudpathlib and protobuf version constraints are documented or if any known incompatibilities exist.
- Whether the Orion Feature Store backend this client targets is actively maintained and production-ready.
What it is and what it does
Orion Python Client is a lightweight wrapper for pushing ML model features from offline data sources (Hive/Delta tables, cloud-stored Parquet/Delta folders) into Orion Feature Store. It handles feature metadata retrieval, protobuf serialization of feature values, and Kafka message production to enable asynchronous feature ingestion pipelines.
The package is designed for teams running feature engineering workflows on Spark who need to serialize and stream features to a centralized feature store. It supports scalar types (FP32, FP64, Int32, Int64, UInt32, UInt64, String, Bool) and vector types, with Kafka acting as the transport layer. You initialize a client with metadata source credentials, call methods to fetch feature schemas and generate protobuf-encoded DataFrames, then push those to Kafka topics for downstream consumption.
Use it for
- Push batch-computed features from Spark DataFrames to Orion Feature Store via Kafka for model serving.
- Retrieve feature metadata and schema information from Orion to validate offline feature computation pipelines.
- Serialize ML feature vectors into protobuf format for efficient Kafka transport.
- Integrate offline feature engineering on Delta/Hive tables with a centralized feature store backend.
- Automate feature ingestion workflows that read from cloud storage and publish to Orion.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already running Spark-based feature engineering and have Orion Feature Store deployed as your backend.
The low install friction and permissive license make it a straightforward addition. However, the aging maintenance status (343 days since last release) means you should verify that it remains compatible with your Spark, Kafka, and Orion versions before committing to production use.
Install
orion-py-client on PyPI
Before you install
Low install friction with only 2 runtime dependencies (cloudpathlib and protobuf). Maintenance status is aging—last release was 343 days ago—so expect slower response to issues or updates.
Requires Python 3.7+; Kafka feature push functionality requires Apache Spark 3.0+ and spark-sql-kafka.
License in practice
Licensed under Apache-2.0 (permissive), so you can use, modify, and distribute this package freely in commercial or private projects without copyleft obligations.
Quickstart
pip install orion-py-client==0.1.14
from orion_py_client import OrionPyClient
client = OrionPyClient(
features_metadata_source_url="your_features_metadata_source_url",
job_id="your_job_id",
job_token="your_job_token"
)
offline_src_type_columns, offline_col_to_default_values_map, entity_column_names = client.get_features_details()Verify before relying
- Whether the package actively maintains compatibility with recent versions of Apache Spark and Kafka ecosystems.
- Whether cloudpathlib and protobuf version constraints are documented or if any known incompatibilities exist.
- Whether the Orion Feature Store backend this client targets is actively maintained and production-ready.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagescloudpathlibprotobuf |
| Maintenance | Aging 343 days since the last release |
| First released | |
| Downloads | 225,969 / month, #9,209 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: orion_py_client-0.1.14-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 store client python”
- orion-py-clientA Python client for pushing ML feature data from offline sources to…
- databricks-feature-storeThis package is deprecated as of v0.17.0; it provides a client for…
- databricks-feature-engineeringDatabricks Feature Engineering client for creating, managing, and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Database packages
psycopg2-binary is a PostgreSQL database adapter for Python that implements the DB API 2.0 specification, enabling Python applications to connect to and query PostgreSQL databases with thread-safe concurrent operations.
Python client library for connecting to and executing commands against Redis key-value stores, supporting both synchronous and asynchronous operations.
Install it if your application needs to interact with Redis; the only prerequisite is a running Redis server instance.
YDB Python SDK is the official client library for connecting to and querying YDB databases from Python applications.
Install it if you need to connect Python applications to YDB databases.
Connects Python applications to Snowflake data warehouses using the DB API 2.0 specification, enabling SQL queries, data transfers, and warehouse operations.
sqlparse tokenizes SQL text into a tree of statements, clauses, and expressions, and provides functions to split scripts, format queries, and inspect parsed tokens without validating dialect or syntax.
Install it if you need to manipulate, format, or analyze SQL text programmatically.
Provides base adapter protocols and shared functionality that database adapters use to integrate with dbt-core, handling connections, dialect translation, relation caching, and core interface management.
See also feast · azureml-featurestore · sagemaker-feature-store-pyspark-3.1 · sagemaker-feature-store-pyspark · confluent-kafka · wmill · kubeflow · sagemaker-feature-store-pyspark-3.3 · mleap · hopsworks