Enhancing Data Lake Access Control with Native IAM Roles #2574
Description
Our current approach to data lake access control heavily relies on manual Access Control Lists (ACLs) applied directly to files and folders within our data lake storage. While functional, this method presents significant challenges:
Scalability Issues: As our data lake grows in size and complexity, and as more teams and users require access, manually managing ACLs becomes increasingly cumbersome and time-consuming.
Increased Risk of Errors: Manual processes are inherently prone to misconfigurations, which can lead to either over-privileged access (security risks) or under-privileged access (hindering productivity).
Lack of Centralized Governance: ACLs are managed at the storage layer, often disconnected from our central identity and access management (IAM) system, making it difficult to enforce consistent security policies.
Auditing Complexity: Tracking and verifying who has access to what data at a granular level becomes a significant manual effort, impacting our ability to demonstrate compliance.
Operational Overhead: The ongoing effort required to maintain and update ACLs diverts resources that could be focused on more strategic data initiatives.
Is it possibleto a role-based access control (RBAC) model for our data lake, leveraging native cloud IAM roles. This approach would link access to the data directly to the IAM roles assigned to users and services, ensuring that the information displayed to the end-user or consumed by an application solely depends on their assigned role, rather than manual file-level configurations.