You need to perform exploratory data analysis (EDA) on a petabyte-scale dataset, without managing cluster infrastructure, paying only for queries you run, and writing analysis in Python from a Jupyter notebook. Which solution satisfies these constraints?

Running Apache Spark from within Amazon Athena allows you to perform petabyte-scale EDA without managing cluster infrastructure. Athena is a serverless query service that lets you analyze data directly in Amazon S3 using standard SQL or Spark, and you pay only for the queries you run. This integrates well with Python and Jupyter notebooks for interactive analysis. Running Apache Spark from within Amazon SageMaker requires provisioning and managing SageMaker Studio or Notebook instances, which doesn't fully align with the "no managing cluster infrastructure" constraint for the Spark execution environment itself. Running Apache Spark on an Amazon EMR cluster means you are explicitly provisioning and managing cluster infrastructure, which violates a key constraint. Using Apache Spark integrated with Amazon Redshift would involve managing a Redshift cluster, again violating the "no managing cluster infrastructure" constraint.