AWS Collaborative Problem-Solving: Tackling Complex Redshift and Lake Formation Challenges

AWS Collaborative Problem-Solving: Tackling Complex Redshift and Lake Formation Challenges

Introduction

In the fast-paced world of cloud computing, challenges often arise that require not just technical expertise but also a collaborative, problem-solving mindset. Recently, I encountered a situation involving the integration of Amazon Redshift, Lake Formation, and a zero-trust CICD setup in a multi-account AWS environment. The complexity of the issue underscored the importance of curiosity, structure, and collaboration in finding meaningful solutions. Here's how we approached it and the lessons learned along the way.

Sharing External Schemas from Redshift

The situation centered on sharing external schemas from Amazon Redshift with another AWS account while adhering to stringent security and governance policies. The setup involved a zero-trust environment, meaning no manual console interactions ("click-ops") were allowed, a Lake Formation governance account, which controlled access to the S3 bucket underpinning the external schema, and CICD pipelines for deploying and managing all resources across multiple AWS accounts.

The challenge? Despite successfully associating the data share, the shared tables weren’t visible in the consumer account. The setup demanded precision in permissions and integration, yet it also needed to be automated within the constraints of the CICD pipeline.

Tackling this problem required a blend of technical rigor and a willingness to explore possibilities. Here’s how we approached it.

Understand the Dependencies

The first step was to map out all the moving parts—Redshift, Lake Formation, S3, and IAM permissions. In zero-trust environments, every interaction between these services must be explicitly defined. For Lake Formation, this meant ensuring the Redshift service role in the consumer account was granted explicit permissions in Lake Formation and permissions were defined using API calls or infrastructure as code, avoiding any manual steps.

Automating Permissions in Lake Formation

Given the zero-trust approach, we proposed using the Lake Formation API to automate permission grants during pipeline deployments. For example, the grant-permissions and batch-grant-permissions APIs allowed the governance account to assign SELECT and DESCRIBE permissions to the Redshift service role in the consumer account.  These API calls could be integrated into the CICD pipeline using Python (boto3), CloudFormation, or Terraform.

Recipe for Automating Lake Formation Permissions

1. Initialize Services
   Set up the Lake Formation API client with governance account credentials.

2. Define Permission Details
   Specify the consumer account, Redshift role, database name, and table name.

3. Create Permission Grant Requests
   Build a request for SELECT and DESCRIBE permissions using the batch-grant-permissions API.

4. Apply Permissions Programmatically
   Execute the permission grant request via API to automate access control.

5. Validate Permissions
   Test the setup by querying the schema in the consumer account and reviewing CloudTrail logs to confirm success.

Here’s a sample Python snippet for automating Lake Formation permissions:

(Python)

import boto3

lakeformation = boto3.client('lakeformation')

response = lakeformation.batch_grant_permissions(
    Entries=[
        {
            'Id': 'unique-id-1',
            'Principal': {
                'DataLakePrincipalIdentifier': 'arn:aws:iam::consumer-account-id:role/RedshiftRole'
            },
            'Resource': {
                'Table': {
                    'DatabaseName': 'database_name',
                    'Name': 'table_name'
                }
            },
            'Permissions': ['SELECT', 'DESCRIBE']
        }
    ]
)
print(response)

Validating Access and Testing Configurations

To ensure the setup worked as expected, we recommended testing each step programmatically. Using the AWS CLI or CloudTrail logs helped confirm that permissions were applied correctly, and data visibility in the consumer account could be validated by querying the shared schema and tables.

Providing Structured, Actionable Guidance

Beyond solving the immediate problem, our goal was to make the solution replicable and clear for future use. This included detailed API call examples for granting permissions, infrastructure as code templates for Lake Formation configurations, and a simple workflow for testing and debugging.

Curiosity and Collaboration Are Key

In complex, multi-service environments, curiosity drives deeper understanding. Collaboration fosters new perspectives and solutions that might not be immediately obvious.

Structure Creates Clarity

Even when exploring unknown territory, having a structured approach—breaking the problem into manageable pieces—ensures no detail is overlooked.

Empathy Matters

When working with clients or teammates, understanding the constraints of their environment (e.g., zero-trust policies, multi-account setups) builds trust and leads to solutions tailored to their needs.

Conclusion

Cloud computing challenges, especially those involving legacy or hybrid systems, can feel overwhelming. But with the right mindset—one of exploration, structure, and collaboration—they become opportunities to learn and grow. Whether you’re working with Redshift, Lake Formation, or any other tool, remember that no problem is unsolvable when approached with curiosity and a willingness to help.

If this resonates with you or your team, feel free to share your own challenges. Let’s continue to foster a community where problem-solving and learning go hand in hand. 🚀☕

Image:  Suresh Anchan from Pixabay

Image:  AWS