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Enforce fine-grained access control on data lake tables using AWS Glue 5.0 integrated with AWS Lake Formation
- AWS Glue 5.0 provides fine-grained access control based on policies defined in AWS Lake Formation for granular control over data lake resources at the table, column, and row levels.
- Lake Formation, a data lake management service, allows you to define fine-grained access controls through grant and revoke statements and automatically enforce those policies using compatible engines.
- Using AWS Glue 5.0 with Lake Formation lets you enforce permissions on each Spark job to apply Lake Formation permissions control when AWS Glue runs jobs.
- To enable Lake Formation FGAC for AWS Glue 5.0 jobs, create a standard Data Catalog table, then register the location, and grant table permissions using Lake Formation.
- You can create PySpark jobs in AWS Glue to process input data, configure FGAC on the tables with row and column-based filters and limit read access to specific columns using Lake Formation permissions.
- To enforce FGAC, use Spark SQL and Spark DataFrames and configure Lake Formation FGAC for AWS Glue notebooks through the console.
- AWS Glue 5.0 governs access through a user profile and a system profile driver, delegating table stage reads to system executors.
- Enabling Lake Formation FGAC in AWS Glue jobs makes previously dynamic data Frames with non-delegable operations compatible.
- AWS Glue 5.0 unifies handling of FGAC permissions across service integrations, notably Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
- Through Lake Formation permissions, AWS Glue 5.0 simplifies granular access control to data lake resources at the table, column, and row levels.
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