AWS sagemaker documentation change

Service: sagemaker · 2026-06-28 · Documentation low

File: sagemaker/latest/dg/use-spark-processing-container.md

Summary

Updated documentation link and added new PySparkProcessor code example for SageMaker Python SDK v3 while maintaining legacy v2 reference.

Security assessment

The changes update a documentation URL and add a new SDK v3 code pattern. No security-related content was added or modified. The code example focuses on job parameters like instance configuration without mentioning security controls, IAM permissions, or data protection mechanisms.

Diff

diff --git a/sagemaker/latest/dg/use-spark-processing-container.md b/sagemaker/latest/dg/use-spark-processing-container.md
index 8b2d9799a..1f336e1d9 100644
--- a//sagemaker/latest/dg/use-spark-processing-container.md
+++ b//sagemaker/latest/dg/use-spark-processing-container.md
@@ -11 +11 @@ Apache Spark is a unified analytics engine for large-scale data processing. Amaz
-With the [Amazon SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk#installing-the-sagemaker-python-sdk), you can easily apply data transformations and extract features (feature engineering) using the Spark framework. For information about using the SageMaker Python SDK to run Spark processing jobs, see [Data Processing with Spark](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html#data-processing-with-spark) in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/).
+With the [Amazon SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk#installing-the-sagemaker-python-sdk), you can easily apply data transformations and extract features (feature engineering) using the Spark framework. For information about using the SageMaker Python SDK to run Spark processing jobs, see [Data Processing with Spark](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html#data-processing-with-spark) in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/).
@@ -18,0 +19,26 @@ The following code example shows how to run a processing job that invokes your P
+SageMaker Python SDK v3
+    
+    
+    
+    from sagemaker.core.spark.processing import PySparkProcessor
+    
+    spark_processor = PySparkProcessor(
+        base_job_name="spark-preprocessor",
+        framework_version="2.4",
+        role=role,
+        instance_count=2,
+        instance_type="ml.m5.xlarge",
+        max_runtime_in_seconds=1200,
+    )
+    
+    spark_processor.run(
+        submit_app="preprocess.py",
+        arguments=['s3_input_bucket', bucket,
+                   's3_input_key_prefix', input_prefix,
+                   's3_output_bucket', bucket,
+                   's3_output_key_prefix', output_prefix],
+    )
+
+SageMaker Python SDK v2 (Legacy)
+    
+