Configure seamless single sign-on with SQL analytics in Amazon SageMaker Unified Studio

Amazon SageMaker Unified Studio supplies a unified expertise for utilizing information, analytics, and AI capabilities. SageMaker Unified Studio now helps trusted identification propagation (TIP) for SQL workloads, enabling fine-grained information entry management based mostly on particular person person identities. Organizations can use this integration to handle information permissions via AWS Lake Formation whereas utilizing their present single sign-on (SSO) infrastructure.

Organizations already utilizing Amazon Redshift with TIP can lengthen their present Lake Formation permissions to SageMaker Unified Studio. Customers merely log in via SSO and entry their approved information utilizing the SQL editor, sustaining constant safety controls throughout their analytics surroundings.

This publish demonstrates how you can configure SageMaker Unified Studio with SSO, arrange initiatives and person onboarding, and entry information securely utilizing built-in analytics instruments.

Answer overview

For our use case, a retail company is planning to implement gross sales analytics to establish gross sales patterns and product classes which can be doing nicely. It will assist the gross sales group enhance on gross sales planning with focused promotions and assist the finance group plan budgeting with higher stock administration. The company shops a buyer desk in an Amazon Easy Storage Service (Amazon S3) information lake and a store_sales desk in a Redshift cluster.

The company makes use of SageMaker Unified Studio because the UI, with customers onboarded from their identification supplier (IdP) to AWS IAM Id Heart with TIP. Amazon SageMaker Lakehouse centralizes information from Amazon S3 and Amazon Redshift, and Lake Formation supplies fine-grained entry management based mostly on person identification. For our instance use case, we discover two totally different customers. The next desk summarizes their roles, the instruments they use, and their information entry.

Person	Group	Instrument	Knowledge Entry
Ethan (Knowledge Analyst)	Gross sales	Amazon Athena for interactive SQL evaluation	Non-sensitive buyer information (`id`, `c_country`, `birth_year`) and `store_sales` full desk entry
Frank (BI Analyst)	Finance	Amazon Redshift for experiences and visualization	US buyer information (`c_country='US'`)

The next diagram illustrates the answer structure.

SageMaker Unified Studio with IAM Id Heart simplifies the person journey from authentication to information evaluation. The workflow consists of the next steps:

Customers register with organizational SSO credentials via their IdP and are redirected to SageMaker Unified Studio.
Customers configure IAM Id Heart authentication for Amazon Redshift, linking identification administration with information entry.
Customers entry the question editor for Amazon Redshift or SageMaker Lakehouse, triggering IAM Id Heart federation to generate session and entry tokens.
SageMaker Unified Studio retrieves person authorization particulars and group membership utilizing the session token.
Customers are authenticated as IAM Id Heart customers, able to discover and analyze information utilizing Amazon Redshift and Amazon Athena.

To implement our answer, we stroll via the next high-level steps:

Arrange SageMaker Lakehouse sources.
Create a SageMaker Unified Studio area with SSO and TIP enabled.
Configure Amazon Redshift for TIP and validate entry.
Validate information entry utilizing Amazon Athena.

Conditions

Earlier than you start implementing the answer, you have to have the next in place:

If you happen to don’t have an AWS account, you may enroll for one.
We offer utility scripts to assist arrange varied sections of the publish. To make use of them:

Proper-click this hyperlink and save the utility scripts zip file.
Unzip the file to a terminal that has the AWS Command Line Interface (AWS CLI) configured. You may as well use AWS CloudShell.
Run the scripts solely when prompted within the related sections.

Notice: The utility scripts are configured for
us-east-1 area. If you happen to want one other area, edit the area within the scripts earlier than working them.

To deploy the infrastructure, right-click this hyperlink and choose ‘Save Hyperlink As’ to reserve it as sagemaker-unified-studio-infrastructure.yaml. Then add the file when creating a brand new stack within the AWS CloudFormation console, which is able to create the next sources:
1. An S3 bucket to carry the shopper information used on this publish.
2. An AWS Id and Entry Administration (IAM) position known as DataTransferRole with permissions as outlined in Conditions for managing Amazon Redshift namespaces within the AWS Glue Knowledge Catalog.
3. An IAM position known as IAMIDCRedshiftRole, which will likely be used later to arrange the IAM Id Heart Redshift software.
4. An IAM position known as LakeFormationRegistrationRole, following the directions in Necessities for roles used to register places, and needed IAM insurance policies.
If you happen to don’t have a Lake Formation person, you may create one. For this publish, we use an admin person. For directions, see Create a knowledge lake administrator.
If IAM Id Heart isn’t enabled, confer with Enabling AWS IAM Id Heart for directions to allow it.
1. If it’s essential to migrate present Redshift customers and teams, use the IAM Id Heart Redshift migration utility.
2. For a fast option to check the function and familiarize your self with the method, we offer a script to generate mock customers and teams. Run the setup-idc.sh script, which is offered in Step 2, to create check customers and teams in IAM Id Heart for demonstration functions.
Combine IAM Id Heart with Lake Formation. For directions, see Connecting Lake Formation with IAM Id Heart.
Register the S3 bucket as a knowledge lake location:
1. On the Lake Formation console, select Knowledge lake places within the navigation pane.
2. Select Register location.
3. For the position, use LakeFormationRegistrationRole.
Create an IAM Id Heart Redshift software, as detailed in our earlier publish:
1. On the Amazon Redshift console, select IAM Id Heart connections within the navigation pane and select Create software.
2. For each the show identify and software identify, enter redshift-idc-app.
3. Set the IdP namespace to awsidc.
4. Select IAMIDCRedshiftRole because the IAM position.
5. Select Subsequent to create the appliance.
6. Be aware of the appliance Amazon Useful resource Title (ARN) to make use of in subsequent steps. The ARN format is arn:aws:sso:::software/ssoins-/apl-.
If you happen to don’t have present Redshift tables to work with, run the script setup-producer-redshift.sh, which is offered in Step 2, to create a producer namespace and workgroup, arrange a pattern gross sales database, and generate needed tables with check information.
The publish additionally makes use of simulated buyer information saved within the AWS Glue Knowledge Catalog. To arrange this information and configure the required Lake Formation permissions, run the setup-glue-tables-and-access.sh script offered in Step 2.

Arrange SageMaker Lakehouse sources

On this part, we configure the foundational lakehouse sources required for SageMaker to entry and analyze information throughout a number of storage programs. We’ll register the Redshift occasion to the AWS Glue Knowledge Catalog to make warehouse information discoverable and set up Lake Formation permissions on lakehouse sources for person identities to make sure safe, ruled entry to each information lake and information warehouse sources from inside SageMaker environments.

Register Redshift occasion to the Knowledge Catalog

On this step, we use the store_sales information, which we created earlier utilizing the setup-producer-redshift.sh script. You may register complete clusters to the Knowledge Catalog and create catalogs managed by AWS Glue. To register a cluster to the Knowledge Catalog, full the next steps:

On the Lake Formation console, select Administrative roles and duties within the navigation pane.
Below Knowledge lake directors, select Add.
Select Learn-only administrator, then select AWSServiceRoleForRedshift.
On the Amazon Redshift console, open your namespace.
On the Actions dropdown menu, selected Register with AWS Glue Knowledge Catalog, then select Register.
Register to the Lake Formation console as the information lake administrator and select Catalogs within the navigation pane.
Below Pending catalog invites, choose the namespace and settle for the invitation by selecting Approve and create catalog.
Present the identify for the catalog as salescatalog.
Choose Entry this catalog from Apache Iceberg appropriate engines, select DataTransferRole for the IAM position, then select Subsequent.
Select Add permissions and select the admin IAM position below IAM customers and roles.
Choose Tremendous person for catalog permissions and select Add.
Select Subsequent.
Select Create catalog.

Arrange Lake Formation permission on lakehouse sources for person identities

On this part, we configure Lake Formation permissions to allow safe entry to lakehouse sources for federated person identities. Lake Formation supplies fine-grained entry management that works seamlessly with IAM Id Heart, permitting you to handle permissions centrally whereas sustaining safety boundaries.

We’ll give attention to granting database entry to IAM Id Heart teams in Lake Formation and setting table-level permissions for federated Redshift catalog tables. These permissions type the safety basis for our federated question structure, enabling customers to seamlessly entry each S3 information lake and Redshift information warehouse sources via a unified interface.

Grant database entry to IAM Id Heart teams in Lake Formation

After you share your Redshift catalog with the Knowledge Catalog and combine with Lake Formation, you have to grant acceptable database entry. Comply with these steps to arrange permissions in your information lake sources for company identities:

On the Lake Formation console, below Permissions within the navigation pane, select Knowledge permissions.
Select Grant.
Choose Principals for Principal sort.
Below Principals, choose IAM Id Heart and select Add.
Within the pop-up window, if that is your first time assigning customers and teams, select Get began.
Seek for and choose the IAM Id Heart teams awssso-sales and awssso-finance.
Select Assign.
Below LF-Tags or catalog sources, select Named Knowledge Catalog sources.
1. Select :salescatalog/dev for Catalogs.
2. Select sales_schema for Database.
Below Database permissions, choose Describe.
Select Grant to use the permissions.

Grant table-level permissions for federated Redshift catalog tables

Full the next steps to grant desk permissions to the IAM Id Heart teams:

On the Lake Formation console, below Permissions within the navigation pane, select Knowledge permissions.
Select Grant.
Choose Principals for Principal sort.
Below Principals, choose IAM Id Heart and select Add.
Within the pop-up window, if that is your first time assigning customers and teams, select Get began.
Seek for and choose the IAM Id Heart group awssso-sales.
Select Assign.
Below LF-Tags or catalog sources, select Named Knowledge Catalog sources.
1. Select :salescatalog/dev for Catalogs.
2. Select sales_schema for Database.
3. Select store_sales for Desk.
Choose Choose and Describe for Desk permissions.
Select Grant to use the permissions.

Create a SageMaker Unified Studio area with SSO and TIP enabled

For directions to create a SageMaker Unified Studio area, confer with Create an Amazon SageMaker Unified Studio area – fast setup. As a result of your IAM Id Heart integration is already full, you may specify an IAM Id Heart person within the area configuration settings.

Allow TIP in SageMaker Unified Studio

Full the next steps to allow TIP in SageMaker Unified Studio:

On the SageMaker console, use the AWS Area selector within the prime navigation bar to decide on the suitable Area.
Select View domains and select the area’s identify from the checklist.
On the area’s particulars web page, on the Venture profiles tab, select a mission profile, for instance, SQL analytics.
Choose SQL analytics and select Edit.
Within the Blueprint parameters part, choose enableTrustedIdentityPropagationPermissions and select Edit.
Replace the worth as true.
To implement authorization-based on TIP, the SageMaker Unified Studio admin could make this parameter non-editable.
Select Save.

Allow person entry for SageMaker Unified Studio area

Full the next steps to allow person entry for the SageMaker Unified Studio area:

Open the SageMaker console within the acceptable Area and select Domains within the navigation pane.
Select an present SageMaker Unified Studio area the place you wish to add SSO person entry.
On the area’s particulars web page, on the Person administration tab, within the Customers part, select Add and Add SSO customers and teams.
Select the person (for this publish, we add the person Frank) from the dropdown checklist and select Add customers and teams.

Add mission members

SageMaker Unified Studio initiatives facilitate group collaboration for various enterprise initiatives. Because the mission proprietor, Ethan now can add Frank as a group member to allow their collaboration. So as to add members to an present mission, full the next steps:

Register to the SageMaker Unified Studio console utilizing the SSO credentials of who owns the mission (for this publish, Ethan).
Select Choose a mission.
Select the mission you wish to edit.
On the Venture overview web page, increase Actions and select Handle members.
Select Add members.
Enter the identify of the person or group you wish to add (for this publish, we add Frank).
Choose Contributor if you wish to add the mission member as a contributor.
(Elective) Repeat these steps so as to add extra mission members. You may add as much as eight mission members at a time.
Select Add members.

Create a SQL analytics mission in Unified Studio

On this step, we federate into SageMaker Unified Studio and create a mission utilizing SQL analytics. Full the next steps:

Federate into SageMaker Unified Studio utilizing your IAM Id Heart credentials:
1. On the SageMaker console, select Domains within the navigation pane.
2. Copy the SageMaker Unified Studio URL to your area and enter it into a brand new browser window.
3. Select Register with SSO.
4. A browser pop-up will redirect you to your most well-liked IdP login web page, the place you enter your IdP credentials.
5. If authentication if profitable, you can be redirected to SageMaker Unified Studio.
After logging in, select Create mission.
Enter a reputation to your mission. This mission identify is ultimate and may’t be modified later.
(Elective) Enter an outline to your mission. You may edit this later.
Select a mission profile. For this demo, we select the SQL analytics profile from the obtainable templates.
Depart the default values as they’re or modify them in line with your use case, then select Proceed.
Select Create mission to finalize the mission and initialize your SQL analytics workspace.

For extra detailed info and superior configurations, confer with Create a mission.

Configure Amazon Redshift for TIP and validate entry

Run the setup-consumer-redshift.sh script (offered within the stipulations). This script will create a brand new namespace and workgroup and add the required tags, which you’ll use later to combine with SageMaker Unified Studio compute.

If you’re creating the cluster manually, add one of many following tags to the Redshift cluster or workgroup that you just wish to add to SageMaker Unified Studio:

Choice 1 – Add a tag to permit solely a selected SageMaker Unified Studio mission to entry it: AmazonDataZoneProject=
Choice 2 – Add a tag to permit all SageMaker Unified Studio initiatives on this account to entry it: for-use-with-all-datazone-projects=true

Create compute utilizing IAM Id Heart authentication

After you arrange your mission, the subsequent step is to ascertain a compute useful resource connection on the SageMaker Unified Studio console. Comply with these steps so as to add both Amazon Redshift Serverless or a provisioned cluster to your mission surroundings:

Go to the Compute part of your mission in SageMaker Unified Studio.
On the Knowledge warehouse tab, select Add compute.
You may create a brand new compute useful resource or select an present one. For this publish, we select Hook up with present compute sources, then select Subsequent.
Select the kind of compute useful resource you wish to add, then select Subsequent. For this publish, we select Redshift Serverless.
Below Connection properties, present the JDBC URL or the compute you wish to add, which is built-in with IAM Id Heart. If the compute useful resource is in the identical account as your SageMaker Unified Studio mission, you may choose the compute useful resource from the dropdown menu. In our instance, we use the buyer account that was simply provisioned.
Below Authentication, choose IAM Id Heart.
For Title, enter the identify of the Redshift Serverless or provisioned cluster you wish to add.
For Description, enter an outline of the compute useful resource.
Select Add compute.

The SageMaker Unified Studio Venture Compute and Knowledge pages will now show info for that useful resource.

If all the things is configured appropriately, your compute will likely be created utilizing IAM Id Heart. As a result of your IdP credentials are already cached whilst you’re logged in to SageMaker Unified Studio, it makes use of the identical credentials and creates the compute.

Check information entry utilizing Amazon Redshift

When Ethan logs in to SageMaker Unified Studio utilizing IAM Id Heart authentication, he efficiently federates and may entry buyer information from all international locations however just for non-sensitive columns. Let’s hook up with Amazon Redshift in SageMaker Unified Studio by following these steps:

Select Actions and select Open Question editor.
Select Redshift within the Knowledge explorer pane.
Run the shopper gross sales calculation question to watch that person Ethan (a knowledge analyst) can entry buyer information from all international locations however solely non-sensitive columns (id, birth_country, product_id):
```
choose current_user, c.*, sum(s.sales_amount) as total_sales from "awsdatacatalog"."customerdb"."buyer" c be a part of "dev@salescatalog"."sales_schema"."store_sales" s  on c.id=s.id group by all;
```

You’ve gotten efficiently configured Redshift to make use of IAM Id Heart authentication in SageMaker Unified Studio.

Validate information entry utilizing Amazon Athena

When Frank logs in to SageMaker Unified Studio utilizing IAM Id Heart authentication, he efficiently federates and may entry buyer information just for the US. To question with Athena, full the next steps:

Select Actions and select Open Question editor.
Select Lakehouse within the Knowledge explorer pane.
Discover AwsDataCatalog, increase the database, select the respective desk, and on the choices menu (three dots), select Preview information.

The next demonstration illustrates how person Frank, a BI analyst, can carry out SQL evaluation utilizing Athena. Resulting from row-level filtering applied via Lake Formation, Frank’s entry is restricted to buyer information from the US solely. Moreover, you may observe that within the Knowledge explorer pane, Frank can solely view the customerdb database. The dev@salescatalog database isn’t seen to Frank as a result of no entry has been granted to his respective group from Lake Formation.

The IAM Id Heart authentication integration is full; you need to use each Amazon Redshift and Athena via SageMaker Unified Studio in a simplified, all-in-one interface.Notice that, on the time of writing, Athena doesn’t work with Redshift Managed Storage (RMS).

Clear up

Full the next steps to scrub up the sources you created as a part of this publish:

Delete the information from the S3 bucket.
Delete the Knowledge Catalog objects.
Delete the Lake Formation sources and Athena account.
Delete the SageMaker Unified Studio mission and related area.
If you happen to created new Redshift cluster for testing this answer, delete the cluster.

Conclusion

On this publish, we offered a complete information to enabling trusted identification propagation inside SageMaker Unified Studio. We lined the setup of a SageMaker Unified Studio area with SSO, the creation of tailor-made initiatives, environment friendly person onboarding with acceptable permissions, and the administration of AWS Glue and Amazon Redshift managed catalog permissions utilizing Lake Formation. By means of sensible examples, we demonstrated how you can use each Amazon Redshift and Athena inside SageMaker Unified Studio, showcasing safe information entry and evaluation capabilities. This strategy helps organizations preserve strict identification controls whereas serving to information scientists and analysts derive worthwhile insights from each information lake and information warehouse environments, supporting each safety and productiveness in machine studying workflows.

For extra info on this integration, confer with Trusted identification propagation.