Here is a summary of the key points from the blog post:

The blog post showcases a demo using AWS Lake Formation to manage permissions in a data lake built on AWS. The infrastructure uses services like Amazon Kinesis, AWS Glue, Amazon S3, and Amazon Athena. An ETL pipeline processes transaction data by joining it with customer data. Lake Formation is then used to grant fine-grained access to the processed data.

The post explains the differences between IAM and Lake Formation for access control. IAM handles API-level permissions for AWS services, while Lake Formation manages data access in the data catalog and S3 down to column and row level.

The demo grants an IAM role meant to simulate a data analyst limited permissions to only view certain columns and rows in the processed data based on criteria like the customer’s country. Data filters in Lake Formation enable this row and column-level security.

The post also mentions the possibility of using tag-based permissions with Lake Formation to handle access control in a more scalable way as data sources grow.