Use Apache Spark on Amazon EMR Serverless directly from Amazon Sagemaker Studio
News
This article announces the ability to run Apache Spark on Amazon EMR Serverless directly from Amazon SageMaker Studio notebooks, enabling petabyte-scale data analytics and machine learning.
Specifically, the article covers:
- EMR Serverless automatically provisions and scales resources, allowing users to focus on data and models without managing clusters
- Users can create and browse EMR Serverless applications directly from SageMaker Studio and connect to them with a few clicks
- Once connected, users can use Spark SQL, Scala, Python to interactively query, explore, and visualize data, and run Apache Spark jobs
- Jobs run faster due to EMR's performance-optimized versions of Spark (e.g., 4.5x faster than open-source Spark on EMR 7.1)
- EMR Serverless offers fine-grained automatic scaling and users pay for only what they use
- This feature is supported on SageMaker Distribution 1.10+ in all regions where SageMaker Studio is available
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