🎉 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 ⚡

🌊 Introducing Atlan Lakehouse

Atlan captures rich context about assets across your data estate, including definitions, tags, ownership, usage, and lineage, which you can use to answer core governance and adoption questions with confidence. 

Historically, teams have had to wait hours or days to export this metadata from Atlan, load it into their compute platform, and only then start building reports or AI applications to understand how their data estate is being used.

Atlan Lakehouse is a new way to work with Atlan. We now store this rich context about your data estate in an open, interoperable lakehouse that’s instantly accessible from your preferred engines, so you can build dashboards and AI applications on Atlan context immediately, without custom exports or pipelines.

🎉 What's new

You can now enable Lakehouse for your Atlan tenant via Atlan’s App Marketplace and start building dashboards and AI applications on your Atlan context in minutes.

✨ Let’s dig deeper

Atlan Lakehouse is an Apache Iceberg–based data lakehouse that contains everything Atlan knows about your data estate, exposed through an Iceberg REST catalog that your engines can query like any other source.

Just like the rest of Atlan’s platform, Atlan manages the infrastructure behind the scenes so you can focus on connecting your warehouse and building reports, dashboards, and AI applications. Once Lakehouse is enabled, you can instantly power AI and advanced analytics with rich Atlan context:

• Treat metadata like data
  Unify technical, business, governance, and operational signals in a high-performance repository that can feed AI models, contextual search, and agentic workflows across your stack.
• Answer governance and adoption questions easily
  Answer questions like “What percentage of assets are documented or tagged?”, “Which domains have the most stale tables?”, or “Where are critical data quality gaps?” directly from Snowflake or other supported engines – without building export jobs.
• Use your preferred Iceberg REST–compatible engine
  Use any Iceberg REST-compatible engine (for example Snowflake, Spark, Trino) so Atlan context shows up alongside your existing data sources and follows the same analytics patterns you already use.

👏 Give it a shot

If you are an Admin on Atlan, you can enable Atlan Lakehouse for your tenant:

1. In Atlan, go to Workflows > Marketplace.
2. Find the Atlan Lakehouse app tile.
3. Click Enable. We'll notify you when your Lakehouse is ready.

Once your Lakehouse is ready, you can connect any Iceberg REST-compatible engine and start building dashboards and AI applications on your Atlan context.

📘 Learn more

Visit the docs to learn more about popular use cases, including sample queries you can copy and paste to jump-start governance dashboards, adoption reports, and AI applications.

We are excited to share that Atlan has officially launched a revamped in-product dbt experience designed to give you cleaner metadata, clearer lineage, and dramatically improved troubleshooting workflows. This upgrade brings your dbt models closer to the native dbt experience, including… 

• environment-specific assets, with a new filtering option and expansive lineage
• table-level sources allowing a full and complete lineage view
• compiled/raw SQL for improved impact analysis
• detailed error messages
• A heads up view of the last 5 tests and their status
• Increased visibility of the status of the most recent test on asset search, profile & lineage
• An improved overall crawler run-time

…all to help you trust, understand, and fix issues faster!

Upgrading is simple. Workflow admins just head to Workflow Center > Manage, open your dbt connection, and click Upgrade to new dbt experience. New connections already run on this improved version, and all future fixes and capabilities will be supported only on the new experience.

Going forward, Atlan will create a separate dbt asset for each environment (e.g., customers – Production and customers – Staging), ensuring each one has accurate metadata and lineage. Some quick callouts: 

1. The first workflow run after upgrading will take longer because old assets are replaced with new ones, which means FQNs, GUIDs, access policies linked to old assets, and any manually applied metadata will need to be updated. 
2. Favorites may also need to be re-added. 
3. If your data products were manually selected, you’ll need to re-select the new assets; filter-based data products will update automatically.

We recommend upgrading soon to take advantage of all enhancements, and note that the previous version will no longer be supported starting February 15, 2026. 

If you need guidance or help planning your migration, see documentation here, reach out to your CSM or contact us at support@atlan.com.

🎉 What’s new

Sharing a suite of enhancements to how Atlan integrates with your Microsoft data estate.

1. Microsoft Fabric

Atlan's Fabric connector is currently in Private Preview. This connector brings Atlan's metadata discovery, governance, and lineage to Fabric's experiences, including Lakehouse, Warehouse, Pipelines, Semantic Models, Power BI reports, dashboards, and pages. Contact your Atlan Customer Success Manager if you want to be part of this preview.

2. Power BI

We've deepened the Power BI integration, including the below (all GA):

- Improving parsing of complex Power BI asset relationships to ensure complex lineage capture, even in tricky cases like special characters in Power Query functions or renamed fields

- Added incremental extraction from Power BI, reducing workflow runtime and metadata freshness in Atlan

 - Refined how Power BI Apps are cataloged in Atlan; only apps from included workspaces will now appear. This change removes empty or misleading entries, ensuring a cleaner, more accurate catalog experience for all users.

3. Microsoft Teams

The Atlan integration with Microsoft Teams has been rebuilt as a Teams Marketplace App, decreasing setup time and increasing stability. If you are using the existing Teams integration, we recommend you switch to this setup!

We’re thrilled to announce that Atlan’s SAP ECC and SAP S/4HANA connectors are now live in Public Preview!

This marks a major milestone in our mission to bring deep metadata discovery, lineage, and governance to the SAP ecosystem — helping enterprises unlock visibility into their most complex, business-critical systems.

Why This Matters

End-to-End SAP Metadata Discovery and Governance
Atlan now natively connects to SAP ECC and SAP S/4HANA to extract comprehensive metadata directly from the application layer — including Components, TCODES, Function Modules, ABAP Programs, application components, tables, views, fields, check tables, CDS views and the relationships between them. This allows teams to understand the “what” behind their SAP Components like FI, LO, MM, SD and CO and how the data flows and is consumed within the modern data stack.

New Features from PrPr Release

Column-Level Check Table Relationships
The connector now automatically discovers column-level check table relationships, enabling precise data lineage and semantic context. 

Application Component Hierarchy
We’ve added Application Component Hierarchy relationships — allowing automatic discovery of related SAP tables, fields, and data objects by business domain or component (FI, MM, SD, LO, PP, PM, etc.).
These hierarchies can now be leveraged to build rich business semantic Data Products in Atlan that align directly with SAP’s organizational structure, driving cleaner discovery and business alignment.  If interested in more automation around this please contact your Atlan account representative.  

This helps users discover and connect transactional and master data across key SAP for use as data products such as:

• Business Partners
• Customer & Vendor Master
• Material Master
• Sales & Purchase Orders
• Work Centers
• G/L Accounts
• Profit Centers
• Maintenance Plans
  …and more.

Smarter Metadata Scoping

New flexibility lets users filter the SAP metadata extraction scope by:

• Application Component(s) – focus on only the modules relevant to your use case (e.g., FI, SD, MM).
• Atlan Asset Types – choose which metadata objects (tables, function modules, Programs, TCODEs, views) to include or exclude based on business requirements.

This enables faster, more targeted metadata ingestion and reduces noise across massive SAP landscapes.

 What’s Next

This Public Preview is just the start of Atlan’s journey in the SAP ecosystem.
Coming soon on our roadmap:

• SAP Business Data Cloud
• SAP BW on HANA
• SAP BW/4HANA
• SAP Datasphere
• SAP Analytics Cloud
• SAP Business Objects

Together, these will provide an end-to-end metadata lineage bridge across SAP’s legacy and modern data SAP Business Data Cloud ecosystems — from SAP to cloud analytics and beyond.

Interested in early access or upcoming connectors?
Reach out to your Atlan Account Representative to learn more or join the next preview cohort.

📘 Learn More

• SAP ECC
• SAP S/4HANA

🎉 What’s New

This release brings a powerful set of upgrades to Atlan's Tableau integration, helping you unlock deeper insights, richer lineage, and greater control over metadata workflows:

✨ Let’s Dig Deeper

• Embedded Dashboard Discovery: Discover embedded Tableau dashboards as first-class assets. Understand how they’re connected to upstream data and where they’re being used.
• End-to-End Column Lineage: Column-level lineage now spans from your Tableau DataSources through Worksheets and into Dashboards, offering a complete picture for impact analysis, troubleshooting, and trust.
• Smarter Custom SQL Lineage: We now detect tables even within nested subqueries, providing a deeper understanding of your Tableau logic and ensuring your lineage graphs are accurate and complete.
• Hierarchical Filters for Playbooks: New hierarchical filtering in playbooks means less manual work:
  • Select a full folder to auto-include all assets inside it
  • Or zoom in on specific sub-folders when needed
  • Great for keeping automation organized and scalable
• Secure Agent Support: Need to connect to a Tableau server behind a firewall or in an on-prem environment? Atlan Secure Agent makes it possible, ensuring secure, seamless metadata extraction without exposing internal systems.
• Seamless Metadata Access in Tableau: Stay in flow: now you can access column descriptions, owners, and data classifications directly within Tableau, embedded into your dashboards and sources. Just click, no tab switching required.

👏 Give It a Shot

Learn more and let us know what you think!

• Tableau Lineage
• Custom SQL
• Troubleshooting Tableau connectivity

🎉 What’s new

We’ve launched a new connector for Informatica Cloud Data Integration (CDI). You can now extract detailed metadata from CDI Mapping Tasks , and visualize complex lineage flows across source and target database systems. This capability further bolsters use-cases around end-to-end lineage including - impact analysis, regulatory reporting, data engineering change management as well as better context for AI use-cases.

✨ Let’s dig deeper

Here’s what you can expect from the new connector:

• Capture metadata from Informatica Cloud Mapping Tasks and underlying Transformations
• Automatically parse SQL Overrides to show accurate data flow
• Resolve parameter references by parsing parameter files
• Get full column-level lineage across source, target and transformations
• Visualize how data flows across systems via Informatica CDI ETL connector

Your CDI based data pipeline is now traceable inside Atlan's lineage canvas.

👏 Give it a shot

Reach out to your account team to enable this connector on your instance.

Post that head to New Workflow > Marketplace and select Informatica CDI Assets to get started.

• Enter your CDI credentials and connection info
• Choose the projects/folders to include/exclude in the scan
• Upload parameter files (if any)
• Run the workflow and browse the resulting assets and lineage

🎉 What’s new
Atlan now supports connecting to Snowflake using OAuth via Microsoft Entra ID—no more managing static credentials.

✨ What’s in Store

• Seamless, token-based authentication with Microsoft Entra ID.
• Supports Client Credentials Flow for secure, non-interactive authentication—ideal for automated metadata extraction
• Perfect for Azure-first organizations looking to streamline Snowflake access securely!
  

👏 Give it a shot
Set up Snowflake OAuth with Microsoft Entra ID today—and let us know what you think!

📘 Learn More:

• Configure OAuth (Client Credentials Flow) with Microsoft Entra ID

🎉 What’s new

We’re excited to unveil significant enhancements to the Databricks connector, elevating the crawler and miner capabilities to drive greater flexibility, governance, and trust across your Databricks landscape.

✨ Let’s dig deeper

• Primary and Foreign Key (PK & FK) Support: We now capture and display PK/FK information from Databricks, helping users identify the unique keys of assets.
• Materialized View Support: We now ingest and catalog Materialized Views created via native Databricks SQL. Users can visualize MV lineage and gain clear insights into their dependencies and upstream sources.
• Enriched DBX Lineage Process: Users can now view enriched Databricks lineage with detailed entity types (e.g., Pipelines, Notebooks) and unique entity IDs, providing clarity and traceability in their data flows.
• Enabling Cloned Catalog Support for Databricks Miners: We now allows configuring alternative catalog for Databricks Miners when access to the default System tables is restricted. This provides controlled access to lineage metadata, ensuring that users only see information relevant to their business entity.
• Improved Preflight Checks: Enhanced preflight validations provide a smoother Databricks onboarding experience in Atlan.

👏 Give it a shot

Learn more and let us know what you think!

• How to set up Databricks 
• What does Atlan crawl from Databricks?
• Create cloned views of system tables
• Troubleshooting Databricks connectivity