## Overview
SageMaker FeatureStore Spark is a connector library for [Amazon SageMaker FeatureStore](https://aws.amazon.com/sagemaker/feature-store/).
With this spark connector, you can easily ingest data to FeatureGroup's online and offline store from Spark `DataFrame`. Also, this connector contains the functionality to automatically load feature definitions to help with creating feature groups.
## Getting Started
Note: For more information about installation, code samples etc, please reference the FeatureStore AWS [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-ingestion-spark-connector-setup.html).
Raw data
{
"_id": null,
"home_page": "",
"name": "sagemaker-feature-store-pyspark-3.2",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "ML Amazon AWS AI FeatureStore SageMaker",
"author": "Amazon Web Services",
"author_email": "",
"download_url": "https://files.pythonhosted.org/packages/33/98/753d5a6092d4b7293483bbd974a349602f82289958bdf7990b03fea454ed/sagemaker_feature_store_pyspark_3.2-1.1.2.tar.gz",
"platform": null,
"description": "## Overview\n\nSageMaker FeatureStore Spark is a connector library for [Amazon SageMaker FeatureStore](https://aws.amazon.com/sagemaker/feature-store/).\n\nWith this spark connector, you can easily ingest data to FeatureGroup's online and offline store from Spark `DataFrame`. Also, this connector contains the functionality to automatically load feature definitions to help with creating feature groups.\n\n## Getting Started\n\nNote: For more information about installation, code samples etc, please reference the FeatureStore AWS [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-ingestion-spark-connector-setup.html).\n\n",
"bugtrack_url": null,
"license": "Apache License 2.0",
"summary": "Amazon SageMaker FeatureStore PySpark Bindings",
"version": "1.1.2",
"project_urls": null,
"split_keywords": [
"ml",
"amazon",
"aws",
"ai",
"featurestore",
"sagemaker"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "3398753d5a6092d4b7293483bbd974a349602f82289958bdf7990b03fea454ed",
"md5": "e406fa184d4b26829ed6aca21abc4c45",
"sha256": "535fed30b94ce5f1a789943c236a552f52ae8eee9b1b9c4114d8feecf16cbcad"
},
"downloads": -1,
"filename": "sagemaker_feature_store_pyspark_3.2-1.1.2.tar.gz",
"has_sig": false,
"md5_digest": "e406fa184d4b26829ed6aca21abc4c45",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 55507330,
"upload_time": "2023-06-05T23:21:51",
"upload_time_iso_8601": "2023-06-05T23:21:51.283114Z",
"url": "https://files.pythonhosted.org/packages/33/98/753d5a6092d4b7293483bbd974a349602f82289958bdf7990b03fea454ed/sagemaker_feature_store_pyspark_3.2-1.1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-06-05 23:21:51",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "sagemaker-feature-store-pyspark-3.2"
}