Develop and monitor a Spark application using existing data in Amazon S3 with Amazon SageMaker Unified Studio
Big Data Blog
This article demonstrates how to develop and monitor a Spark application using Amazon SageMaker Unified Studio and EMR Serverless, addressing big data analytics challenges faced by organizations.
- Uses EMR Serverless for dynamic resource allocation and simplified cluster management
- Enables development of Spark applications directly in SageMaker Unified Studio
- Provides integrated monitoring through Spark UI and driver logs
- Demonstrates using TPC-DS dataset for building and running Spark queries
- Offers workflow scheduling capabilities through Amazon Managed Workflows for Apache Airflow (MWAA)
The solution provides a unified development environment that streamlines analytics workflows, reduces operational overhead, and enables data teams to focus on insights rather than infrastructure management.
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