Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. We recently introduced the ability to visually browse and connect to Amazon EMR clusters right from the Studio notebook. Starting today, you can now monitor and debug your Spark jobs running on Amazon EMR from Studio notebooks with just a single click. Additionally, you can now discover, connect to, create, stop, and manage EMR clusters directly from Studio.
We demonstrate these newly introduced capabilities in this two-part post.
Analyzing, transforming, and preparing large amounts of data is a foundational step of any data science and ML workflow. Data workers such as data scientists and data engineers use Apache Spark, Hive, and Presto running on Amazon EMR for fast

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