sagemaker-feature-store-pyspark-3.3
Amazon SageMaker FeatureStore PySpark Bindings
Decision gist · record as of 2026-08-14
Yes, if you are already running Spark 3.3 workloads on AWS and need to ingest features into SageMaker FeatureStore—it eliminates manual API calls and simplifies the integration. However, proceed with caution: maintenance is dormant (554 days since last release), so compatibility with recent SageMaker or Spark updates is uncertain. Verify compatibility with your SageMaker API version before adopting in production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Spark 3.3 and an active AWS SageMaker FeatureStore setup with appropriate IAM credentials configured.
- High install friction due to the distribution format (tar.gz source archive).
- Maintenance is dormant—no commits tracked and last release was 554 days ago, suggesting the package receives minimal active development or updates.
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0, a permissive open-source license that allows commercial and private use with minimal restrictions, though you must include a copy of the license and note any modifications.
last release 2025-02-06 (554 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 100,317 downloads/mo, #12,993 on PyPI
Alternatives
Verify before relying
# Install
pip install sagemaker-feature-store-pyspark-3-3
# Import and use with Spark DataFrame
from sagemaker_feature_store_pyspark import FeatureStoreConnector
# Ingest DataFrame to FeatureStore
connector = FeatureStoreConnector()
connector.ingest_data(spark_dataframe, feature_group_name)- Whether PySpark 3.3 is a hard requirement or if the connector works with other Spark versions
- Minimum Python version required (not specified in metadata)
- Current compatibility with recent SageMaker API versions given the dormant maintenance status
What it is and what it does
This package is a PySpark connector that bridges Apache Spark and Amazon SageMaker FeatureStore, enabling you to push feature data from Spark DataFrames directly into SageMaker's online and offline feature stores. It handles the translation between Spark's distributed data model and SageMaker's feature ingestion API, and includes utilities to automatically load feature definitions to simplify feature group creation.
The connector is designed for batch ingestion workflows where you have feature data in Spark and need to materialize it into SageMaker FeatureStore for model training or serving. It abstracts away the details of the SageMaker API calls, letting you work primarily with familiar Spark DataFrame operations.
Use it for
- Batch ingest feature data from a Spark cluster into SageMaker FeatureStore's online and offline stores for ML model training.
- Automatically load and register feature definitions from Spark DataFrames to create SageMaker feature groups without manual schema definition.
- Integrate Spark-based ETL pipelines with SageMaker's feature management infrastructure for centralized feature governance.
- Populate both online (low-latency) and offline (batch) feature stores from a single Spark job to keep training and serving features in sync.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already running Spark 3.3 workloads on AWS and need to ingest features into SageMaker FeatureStore—it eliminates manual API calls and simplifies the integration.
However, proceed with caution: maintenance is dormant (554 days since last release), so compatibility with recent SageMaker or Spark updates is uncertain. Verify compatibility with your SageMaker API version before adopting in production.
Install
sagemaker-feature-store-pyspark-3-3 on PyPI
Before you install
High install friction due to the distribution format (tar.gz source archive). Maintenance is dormant—no commits tracked and last release was 554 days ago, suggesting the package receives minimal active development or updates.
Requires Apache Spark 3.3 and an active AWS SageMaker FeatureStore setup with appropriate IAM credentials configured.
License in practice
Licensed under Apache License 2.0, a permissive open-source license that allows commercial and private use with minimal restrictions, though you must include a copy of the license and note any modifications.
Quickstart
# Install
pip install sagemaker-feature-store-pyspark-3-3
# Import and use with Spark DataFrame
from sagemaker_feature_store_pyspark import FeatureStoreConnector
# Ingest DataFrame to FeatureStore
connector = FeatureStoreConnector()
connector.ingest_data(spark_dataframe, feature_group_name)Verify before relying
- Whether PySpark 3.3 is a hard requirement or if the connector works with other Spark versions
- Minimum Python version required (not specified in metadata)
- Current compatibility with recent SageMaker API versions given the dormant maintenance status
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 | 100,317 / month, #12,993 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_3-1.1.3.tar.gz
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See also sagemaker-feature-store-pyspark · sagemaker-feature-store-pyspark-3.1 · sagemaker-datawrangler · sagemaker-train · sagemaker-training · awsglue-dev · pyspark-client · pyspark-pandas · azureml-featurestore · pyspark-huggingface