🌊 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.

🎉 What’s new

You can now filter assets using Glossaries and Categories right from the Assets page in Atlan!
This enhancement brings powerful semantic filtering to your fingertips—making it easier to discover categories and terms under a glossary.

✨ Let’s dig deeper

Whether you're trying to find all terms grouped under a particular business category or find all categories under a glossary, these new filters make your discovery faster and more intuitive.

🧭 New Filters on the Assets Page

You’ll now see two additional filter options:

• Glossary: View all glossaries and drill down to specific categories within each
• Category: See all categories and explore terms nested under them

Selecting the newly added glossary or category filters the asset list to show categories and terms tagged with that specific glossary or category respectively.

🔍 Why it matters

• 🔎 Easily track categories and terms governed by business glossaries
• 🧠 Understand how metadata terms are applied across data assets

These filters expand the reach of glossaries on the assets page, helping technical users and business users collaborate better.

👏 Give it a shot

Head over to the Assets page, open the Filters panel, and try filtering by your organization's glossary or category terms.
It’s now easier than ever to explore your data using the business language your teams already understand.

Let us know how it helps you cut through the clutter and find what you need faster!

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

Make your ReadMes and rich text content pop!

With new Badges, Buttons, and Asset Cards, you can now add interactive and visual elements to your rich text content—making it easier to highlight key information, drive action, and improve navigability across your metadata experiences.

✨ Let’s dig deeper

Until now, ReadMes and custom metadata often relied on plain text—making it harder to spotlight important notes, add useful CTAs, or reference related assets. That changes today.

🏷️ Badges

Use badges to highlight key terms, statuses, or definitions.

• Add via markdown:
  ![badge](Your badge message)
• Or use /badge directly in the Rich Text editor

🔘 Buttons

Insert call-to-action buttons right inside your documentation—for links, requests, or redirections.

• Add via markdown:
  ![button](Your button label)
• Or use /button in the editor

🧩 Asset Cards

Paste any asset URL or GTC URL from the same Atlan tenant, and it will automatically render as a rich Asset Card, complete with name, icon, and a preview of key details.

No more messy links—just clean, contextual references to other assets.

🌍 Where does this work?

These enhancements are available everywhere Rich Text is supported, including:

• ✅ Custom Metadata (Rich Text properties)
• ✅ ReadMe editor for assets
• ✅ Product ReadMes
• ✅ ReadMe templates

Why it matters: This makes your documentation more engaging, more structured, and way more useful—especially for consumers trying to understand data context at a glance.

👏 Give it a shot

Jump into any ReadMe or Rich Text field and try out a badge, button, or paste an asset URL to see it transform.
With these upgrades, your metadata storytelling just got a major boost ✨

Let us know how you’re using them—we’d love to see what you build!

Workflows New 

🎉 What’s new

We’ve launched a new connector for AlloyDB (PostgreSQL). You can now securely scan metadata from AlloyDB and ingest into Atlan.

✨ Let’s dig deeper

• Connect your AlloyDB instance using a direct connectivity approach or via Self-Deployed Runtime
• Choose from a variety of authentication mechanisms, including Workload Identity Federation for GKE
• Automatically sync schemas, tables, columns so you have a complete picture of your AlloyDB assets in Atlan
• Apply Atlan’s governance features — terms, classifications, owners, policies — to your AlloyDB assets

👏 Give it a shot

A step-by-step setup guide is available as part of the AlloyDB (PostgreSQL) documentation

When viewing asset profiles, users often see technical identifiers for users and groups - like john.doe or tenant-cdxgl1. These make ownership challenging, especially for business users trying to understand who owns or maintains an asset.

• Human-readable names everywhere: Show First + Last Name for users and display names for groups.
• Consistent experience: Visible across Owners, Data Access tab, Activity tab, Custom metadata & Filters.

Give it a shot

This is currently configurable by Admins in Labs ("Show full user and group names"), default toggled off for now. Pending any concerns it will become the default and only option in the coming weeks.

We’re excited to introduce Atlan AI integration with the Glossary. This feature is available to customers with Atlan AI and can be enabled by Admins by switching on the Atlan AI Summary toggle in the Atlan AI section under Labs.

What’s New?

1. Surface what matters quicker

Get crisp, intelligent summaries that surface the most important information without reading long descriptions. Every Glossary, Category, and Term now includes an AI generated overview that makes metadata browsing faster and more intuitive.

2. Smart, automated enrichment insights

If you’re someone who can edit or update a Glossary, Atlan AI now provides smart insights at an aggregate level to help improve Glossary completeness. It highlights areas that are missing descriptions, READMEs, or are still in draft status, so you know exactly what to fix.

When metadata gaps are detected and the Enrich Metadata Labs toggle is also enabled, the summary also provides contextual calls to action: Add Description with AI assistance and Add README using Atlan AI.

3. Explore faster with conversational search

For customers in the Conversational AI Private Preview, Glossary will also surface suggested questions you can ask in natural language, powered by our copilot-like conversational experience. If you’re not part of the Private Preview but would like to be, please reach out to your Customer Success Manager.

See more here!

🎉 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!

🎉 What’s new

Introducing Downstream Activity on Data Products!

You can now get a complete picture of where and how your data products are being used—with visibility into downstream data products in their lineage. This helps teams better understand impact, dependency, and reach across the data ecosystem.

✨ Let’s dig deeper

When working with data products, it’s critical to understand their downstream relationships—especially when planning updates, monitoring usage, or investigating quality issues. With this enhancement, Atlan now shows you:

🔁 Downstream Data Products

• A dedicated Downstream panel now appears on the data product page
• It lists all other data products that depend on or use this data product
• You'll see linked product names, ownership, and the containing domain/subdomain—so you can quickly trace the flow of data and responsibility

🔍 Complementary Insights

Alongside the downstream view, you could already see:

• Activity log: Who changed what and when
• Views: Which users are actively engaging with the product
• Editors: Who last updated the product and when

Together, these views give you a 360° view of your data product’s lifecycle and impact.

Why this matters: Whether you're troubleshooting an issue, planning a schema update, or just documenting impact, downstream visibility helps avoid surprises and drive better decisions.

👏 Give it a shot

Open any data product and explore its Downstream tab to see what other products rely on it. With this update, you can manage your data supply chain with more confidence than ever.

Let us know how this helps your team work smarter with data products!

We’re thrilled to announce that Data Quality Studio is officially GA (Generally Available)! 🎉

What it is:

Data Quality Studio enables business and data teams to collaborate in one workspace to define, automate, and monitor rules that mirror real-world, business-defined expectations — ensuring analytics and AI run on purpose-built, trustworthy data.

Key capabilities include:

• Business-friendly UI to enable non technical users to setup quality rules.
  
  
• Cloud-native execution of checks directly in Snowflake or Databricks
  
  
• AI-powered rule suggestions based on metadata
  
  
• No-code rule creation, custom SQL support, and smart scheduling
  
  
• Smart notifications through Slack and Teams, with routing based on domain or database
  
  
• Quality signals embedded into reporting to build lasting data trust

Supported platforms:

Data Quality Studio supports both Snowflake and Databricks — checks execute natively within those warehouses. 

Accessing Data Quality Studio:

You can request access via the Labs section in Atlan.

👉 Full documentation is available here.

Upgrade note:

To access the latest GA features of Data Quality Studio, please upgrade to the latest version of the Atlan SDK. This ensures compatibility and unlocks all improvements.

A big thank you ❤️

We extend our heartfelt thanks to our customers and design partners who provided invaluable feedback during the preview phase. Your insights helped refine the product into what it is today.

What’s next:

We have an exciting roadmap ahead — including BigQuery integration, deeper remediation flows, an enhanced AI assistant, and much more. Stay tuned for what’s coming next!