@THIAM HUAT TAN :
When granting Databricks access to an S3 bucket, it is necessary to provide access at the highest level of the bucket because the bucket itself is the logical unit of access control in S3. This means that permissions can only be granted at the bucket level, and not at the object or directory level.
However, you can still limit access to specific directories within the bucket by using IAM policies that restrict access to specific prefixes (directories) within the bucket. This can be achieved by creating a policy that grants permissions only to specific prefixes and then attaching that policy to the Databricks IAM role.
As for granting Databricks access to everything (add, list, modify, delete) at the root level of the AWS account, this may seem like a security concern at first glance, but it is necessary to enable Databricks to perform its intended tasks. However, it is important to note that Databricks does not automatically have access to everything in the AWS account. Instead, access is granted only to specific resources that are required for Databricks to perform its tasks, such as the S3 bucket that contains the data to be processed.
Furthermore, Databricks has built-in security measures to prevent unauthorized access to resources. For example, it uses IAM roles and policies to restrict access to specific resources, and it encrypts data in transit and at rest to ensure data security. Additionally, Databricks provides auditing and monitoring features that allow you to track access to resources and detect any potential security breaches.
