Why Ranger-S3: Addressing Current Limitations

Problem Statement

Organisations increasingly use Amazon S3 as a data lake storage layer, accessed through Hadoop ecosystem tools. However, current Ranger deployments face significant limitations:

Limitation 1: Bypassing Ranger Authorization

Current Behaviour:

• Direct S3 API calls or AWS CLI operations bypass Ranger entirely
• Applications using AWS SDK access S3 with IAM credentials without Ranger oversight

Issues:

• Security gap: No centralised audit trail for S3 access
• Inconsistent enforcement: Same data has different access controls depending on access method
• Compliance risk: Cannot prove who accessed what data in S3

Limitation 2: Dual Permission Management Burden

Current Behaviour:

• Usually, AWS components are handled by DevOps team while different teams handle Hadoop Data Clusters.

Issues:

• Operational Cost: Multiple communication operations required cross-team to make a single change to AWS component permissions.

Limitation 3: No Unified S3 Access Control

Current Behaviour:

• IAM policies control S3 access at AWS level
• Ranger policies control access at the application level
• No coordination between the two authorisation layers

Issues:

• Dual maintenance: Security teams manage policies in two systems
• Permission conflicts: IAM may allow what Ranger denies (or vice versa)
• Operational complexity: Troubleshooting access issues requires checking both systems

Business Justification for Ranger-S3 Plugin

1. Unified Policy Management: Manage both HDFS and S3 access through a single Ranger interface
2. Automated IAM Sync: Ranger policies are automatically translated and applied as IAM policies
3. Reduced Operational Burden: Reduces communication between the DevOps team and the Hadoop cluster management team.
4. Consistency: Ensure Ranger policies are enforced in AWS IAM
5. Simplified Governance: Single source of truth for data access policies
6. Hadoop Ecosystem Integration: Seamless S3 access for Hive, Spark, and other tools