Setting Up VPC Peering for Cross-Account Access of Amazon Redshift Clusters in Amazon SageMaker Studio
Introduction:
In today’s digital era, machine learning (ML) has become a vital tool for businesses across various industries. Amazon SageMaker Studio is a web-based integrated ML development environment that allows users to build, train, and deploy models easily. On the other hand, Amazon Redshift is a reliable and scalable cloud data warehouse. Many organizations prefer using SageMaker Studio to analyze data stored in Redshift. By following the steps outlined in the AWS Well-Architected Framework, organizations can establish a cross-account connection between their Redshift cluster and SageMaker Studio. This guide provides step-by-step instructions for setting up this connection, ensuring data security and optimized workflows for ML projects. Prerequisites include launching Redshift in a private subnet and creating a VPC with private and public subnets in the SageMaker account. To successfully establish the connection, organizations need to set up SageMaker Studio with VPCOnly mode and configure SourceIdentity in the SageMaker Studio domain. Additionally, creating an IAM role in the Redshift account and updating the SageMaker IAM execution role are necessary. Followers of these instructions can query Redshift in SageMaker Studio and enhance their data analysis capabilities.
Full Article: Setting Up VPC Peering for Cross-Account Access of Amazon Redshift Clusters in Amazon SageMaker Studio
Cloud Computing and Machine Learning
Machine learning (ML) has become a crucial aspect in various industries, thanks to the availability of compute power and data through cloud computing. It has become an integral part of every business and industry. To facilitate ML development, Amazon has introduced Amazon SageMaker Studio, which is the first fully integrated ML development environment with a web-based visual interface. With SageMaker Studio, developers can perform all ML development steps and have complete control and visibility into the process of building, training, and deploying models.
Using Amazon Redshift with SageMaker Studio
Many organizations prefer using SageMaker Studio to get predictions from data stored in data warehouses such as Amazon Redshift. To ensure proper separation of workloads and simplify cost controls and monitoring between projects and teams, it is recommended to have Amazon Redshift and SageMaker Studio in separate AWS accounts. Additionally, it is advisable to configure Amazon Redshift and SageMaker Studio in VPCs with private subnets for improved security and reduced risk of unauthorized access.
Cross-Account Data Sharing with Amazon Redshift
Amazon Redshift natively supports cross-account data sharing when RA3 node types are used. However, for other node types like DS2 or DC2, VPC peering is required to establish a cross-account connection between Amazon Redshift and SageMaker Studio. In this post, we will provide step-by-step instructions on how to establish a cross-account connection between different Amazon Redshift node types (RA3, DC2, DS2) and SageMaker Studio using VPC peering.
Solution Overview
The solution involves two AWS accounts: a producer account with Amazon Redshift and a consumer account for SageMaker ML use cases with SageMaker Studio set up. Here is a high-level overview of the workflow:
1. Set up SageMaker Studio with VPCOnly mode in the consumer account to ensure all traffic goes through specified VPC and subnets.
2. Update SageMaker Studio domain to turn on SourceIdentity and propagate the user profile name for monitoring and auditing purposes.
3. Create an IAM role in the Amazon Redshift producer account, which SageMaker Studio will assume to access Amazon Redshift.
4. Update the SageMaker IAM execution role in the consumer account, which SageMaker Studio will use to assume the role in the producer Amazon Redshift account.
5. Set up a peering connection between VPCs in the Amazon Redshift producer account and SageMaker Studio consumer account.
6. Query Amazon Redshift in SageMaker Studio in the consumer account.
Prerequisites
Before proceeding, make sure Amazon Redshift is launched in a private subnet in the producer account, which provides an additional layer of security. In the consumer account, create a VPC, private subnet, and public subnet for downloading public libraries. Set up a NAT gateway in the public subnet and add an internet gateway for SageMaker Studio in the private subnet to access the internet.
Setting up SageMaker Studio with VPCOnly Mode
To create SageMaker Studio with VPCOnly mode, follow these steps:
1. Go to the SageMaker console and choose Studio in the navigation pane.
2. Launch SageMaker Studio and select Standard setup, then choose Configure.
3. Choose Create a new role and specify your Amazon S3 buckets, VPC, subnet, and security group.
4. Select VPC Only and proceed with the setup.
Updating SageMaker Studio Domain
To turn on SourceIdentity and propagate the user profile name in SageMaker Studio domain, use the following code:
“`bash
update-domain –domain-id
“`
Create an IAM Role for Amazon Redshift Access
To allow SageMaker Studio to access Amazon Redshift, create an IAM role in the producer account with a custom trust policy and necessary permissions.
Update the SageMaker Execution Role
Next, update the SageMaker IAM execution role in the consumer account to assume the role created for Amazon Redshift access.
Setting Up VPC Peering
Establish a peering connection between the VPCs in the Amazon Redshift producer account and the SageMaker Studio consumer account.
Querying Amazon Redshift in SageMaker Studio
Once the setup is complete, you can query Amazon Redshift in SageMaker Studio in the consumer account.
Conclusion
By following the step-by-step instructions provided in this post, you can establish a cross-account connection between Amazon Redshift and SageMaker Studio. This enables you to leverage the power of machine learning on data stored in Amazon Redshift, while ensuring security and separation of workloads.
Summary: Setting Up VPC Peering for Cross-Account Access of Amazon Redshift Clusters in Amazon SageMaker Studio
With the increasing availability of compute power and data, machine learning (ML) has become a vital part of every industry. Amazon SageMaker Studio is a fully integrated ML development environment that allows you to build, train, and deploy models. Amazon Redshift is a secure and scalable cloud data warehouse. Organizations often use SageMaker Studio to get predictions from data stored in Redshift. By separating workloads across accounts and using VPC peering, organizations can enhance security and simplify cost controls. This article provides step-by-step instructions on how to establish a cross-account connection between Redshift and SageMaker Studio.
Frequently Asked Questions:
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Q2: How does Artificial Intelligence work?
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Q5: Can Artificial Intelligence surpass human intelligence?
A5: While AI has made significant advancements, achieving true human-level intelligence, referred to as artificial general intelligence (AGI), remains a distant goal. AGI would entail machines having the ability to understand, learn, and perform any intellectual task that a human can do. While researchers are working towards AGI, it is currently uncertain when or if it will be achieved.
