sagemaker-feature-store-pyspark-3.1
Amazon SageMaker FeatureStore PySpark Bindings
Decision gist · record as of 2026-08-14
Yes, if you are already running Spark on AWS and have an active SageMaker FeatureStore deployment. No, if you are looking for a general-purpose feature store or do not have Spark infrastructure in place. Caution: the package is dormant (554 days since last release); verify with AWS documentation that it remains supported for your Spark and SageMaker versions before adopting in production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an active Apache Spark environment and AWS SageMaker FeatureStore setup with appropriate IAM credentials configured.
- High install friction: requires a pre-built Spark environment and AWS SageMaker setup.
- Package is dormant (554 days since last release), with no recent maintenance activity visible.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 is permissive; you can use, modify, and distribute this package freely provided you include the license notice and state significant changes.
last release 2025-02-06 (554 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 378,658 downloads/mo, #7,117 on PyPI
Alternatives
Verify before relying
pip install sagemaker-feature-store-pyspark-3-1
from sagemaker_feature_store_pyspark import FeatureStoreManager
# Ingest DataFrame to FeatureGroup online/offline store
manager = FeatureStoreManager()
manager.ingest(spark_dataframe, feature_group_name)- Whether this connector is compatible with Spark versions beyond 3.1 or if it is locked to that version.
- Current maintenance status and whether AWS actively supports this package or recommends alternatives.
- Whether Python version constraints exist despite `requires_python` being unspecified.
What it is and what it does
This package is a Spark connector that bridges Apache Spark DataFrames and Amazon SageMaker FeatureStore. It allows you to ingest feature data directly from Spark into both the online and offline stores of a FeatureGroup, and includes utilities to automatically load feature definitions to streamline FeatureGroup creation.
The connector is designed for teams already running Spark workloads on AWS who want to feed computed features into SageMaker's managed feature store. It requires an existing Spark cluster and SageMaker FeatureStore setup with proper AWS credentials; it is not a standalone tool but rather a bridge between two AWS services.
Use it for
- Batch ingest computed features from a Spark DataFrame into SageMaker FeatureStore for ML model training.
- Load feature definitions automatically from Spark schema to reduce boilerplate when creating new FeatureGroups.
- Synchronize features computed in a Spark pipeline with SageMaker's online store for real-time inference.
- Populate both online and offline stores in a single operation from a Spark job.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already running Spark on AWS and have an active SageMaker FeatureStore deployment.
No, if you are looking for a general-purpose feature store or do not have Spark infrastructure in place. Caution: the package is dormant (554 days since last release); verify with AWS documentation that it remains supported for your Spark and SageMaker versions before adopting in production.
Install
sagemaker-feature-store-pyspark-3-1 on PyPI
Before you install
High install friction: requires a pre-built Spark environment and AWS SageMaker setup. Package is dormant (554 days since last release), with no recent maintenance activity visible.
Requires an active Apache Spark environment and AWS SageMaker FeatureStore setup with appropriate IAM credentials configured.
License in practice
Apache License 2.0 is permissive; you can use, modify, and distribute this package freely provided you include the license notice and state significant changes.
Quickstart
pip install sagemaker-feature-store-pyspark-3-1
from sagemaker_feature_store_pyspark import FeatureStoreManager
# Ingest DataFrame to FeatureGroup online/offline store
manager = FeatureStoreManager()
manager.ingest(spark_dataframe, feature_group_name)Verify before relying
- Whether this connector is compatible with Spark versions beyond 3.1 or if it is locked to that version.
- Current maintenance status and whether AWS actively supports this package or recommends alternatives.
- Whether Python version constraints exist despite `requires_python` being unspecified.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Dormant 554 days since the last release |
| First released | |
| Downloads | 378,658 / month, #7,117 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: sagemaker_feature_store_pyspark_3_1-1.1.3.tar.gz
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See also sagemaker-feature-store-pyspark-3.3 · sagemaker-feature-store-pyspark · sagemaker-datawrangler · azureml-featurestore · sagemaker-data-insights · awsglue-dev · sagemaker-train · orion-py-client · hopsworks · sagemaker-training