🎉 What's new

If you use AWS, your AI/ML teams may be building together in AWS SageMaker Unified Studio. And your context layer lives in Atlan.

As of today, those two are the same thing.

The AWS SageMaker Unified Studio (SMUS) connector is now generally available - built jointly with the AWS team. Every published asset, data product, project, and glossary term from SMUS now flows into the Atlan Context Layer, and the context you govern in Atlan flows back into SMUS automatically. Business stewards and AI/ML teams work on the same governed context, without leaving the tools they already use.

This is what it looks like when a hyperscaler builds natively on the Context Layer for AI.

✨ Let's dig deeper

🔁 Context flows both directions - not just into Atlan Crawl SMUS domains, data products, projects, published & subscribed assets, glossaries, terms, and key column metadata into Atlan. Reverse-sync descriptions you enrich in Atlan back to SMUS projects, published assets, and columns — so the governed context your data stewards define surfaces automatically in the workspace where your AI/ML teams build.

🔗 See how data products are reused across teams Lineage from any published asset surfaces every project that subscribes to it. Cross-team dependencies that were previously invisible become discoverable in a single view, making impact analysis and reuse decisions far easier.

🧭 Fits the domain model you already operate Attach SMUS projects and assets to your existing Atlan Data Domains. No parallel taxonomy, no duplicate ownership, no forced rework of how your business is organized.

🛡️ Enterprise-ready out of the box IAM-based authentication, a supplied CloudFormation template, preflight checks, and on-demand or scheduled crawls — built for production from day one.

🚀 Give it a shot

Setup is self-serve and most teams are connected in a single session.

In Atlan, head to New Workflow → AWS SageMaker Unified Studio. 

Deploy the supplied CloudFormation template in your AWS account, paste the IAM role ARN into Atlan, run the preflight check, and trigger your first crawl!

For more information, see

• 📘 Connector documentation for setup, supported assets, and reverse-sync configuration
•  📝 Joint AWS + Atlan blog for architecture and design rationale

Why Atlan + AWS:
SageMaker Unified Studio gives your AI/ML teams the workspace. Atlan gives them the context that makes the work trustworthy.

The definitions that drift, the lineage that goes untracked, the governance that gets bypassed — that gap is where AI projects lose credibility before they reach production. With SMUS as a first-class citizen in the Atlan Context Layer, the governed context your stewards maintain is the same context your AI/ML teams build on. It doesn't drift, because it only lives in one place.

Atlan is not just another connector in your AWS stack. SMUS connects to Atlan because the Context Layer for AI is where enterprise context compounds — and compounded context is what makes AI work at scale.

Have setup questions or want to talk through scoping? Reach out to your Atlan CSM or Account Manager. For product-related questions, you can also reach out to Bindu Neeharika at bindu.reddy@atlan.com to help you get from CloudFormation template to your first crawl ⚡