Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables data scientists and developers to perform every step of the ML workflow, from preparing data to building, training, tuning, and deploying models. Studio comes with built-in integration with Amazon EMR so that data scientists can interactively prepare data at petabyte scale using open-source frameworks such as Apache Spark, Hive, and300 Presto right from within Studio notebooks. Data is often stored in data lakes managed by AWS Lake Formation, enabling you to apply fine-grained access control through a simple grant or revoke mechanism. We’re excited to announce that Studio now supports applying this fine-grained data access control with Lake Formation when accessing data through Amazon EMR.
Until now, when you ran multiple data processing jobs on an EMR cluster, all the jobs used the same AWS Identity and Access Management (IAM) role for accessing data—namely,

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