Data scientists and ML engineers often need to access raw data files in Amazon Simple Storage Service (Amazon S3) for machine learning training, data exploration, and generative AI workflows. However, when table-level access is governed by AWS Lake Formation, accessing the underlying S3 files has required maintaining separate permission mechanisms. S3 bucket policies or AWS Identity and Access Management (IAM) role policies create operational overhead and risk of permission drift.
Lake Formation now supports direct access to S3 data file locations for tables whose permissions it manages. Previously, data scientists with Lake Formation permissions on AWS Glue Data Catalog tables could query them using spark.sql(). Now, they can also read and write the underlying S3 data files using spark.read.parquet() or spark.read.csv() from Amazon EMR Spark jobs, Amazon SageMaker Unified Studio notebooks with EMR compute, and custom applications. All access is governed by the same…