As a data engineer or governance leader, you'll want visibility into Snowflake user-defined functions (UDFs) because it helps you trace how business logic flows through your pipelines.

You can now discover and govern UDFs as first-class assets in Atlan, with clear relationships to Process assets for lineage tracking. Previously, UDF logic was embedded inside queries and stored procedures with limited visibility in the catalog. Now, UDFs are represented as assets and connected to the Process nodes that execute them.

This means the logic behind your KPIs, masking rules, and reusable transformations is no longer hidden inside code, you can see where it’s used and how it contributes to downstream assets.

✨ Let’s dig deeper

Snowflake Stored Procedures and UDFs work together with Process assets to generate lineage in Atlan.

Here’s what that means in practice:

• UDFs and Stored Procedures are surfaced as assets in your Snowflake connection
• When they’re invoked inside a query, they’re linked to the corresponding Process asset
• You can view which Process assets use a specific UDF or Stored Procedure
• In lineage graphs, you can inspect the Process node to see contributing UDFs and Stored Procedures
• Lineage captures direct calls, dynamic queries and nested calls are not yet supported

For example, if a query calls udtf_complex_dot_star the executed query appears as a Process asset. That Process node shows the relationship to the UDF, helping you understand how transformation logic contributes to downstream tables or dashboards.

This keeps lineage compact and execution-focused, while still exposing the procedural and functional logic behind your data flows.

👏 Give it a shot

To explore UDF and Stored Procedure relationships:

1. Go to your Snowflake connection and open a UDF or Stored Procedure asset.
2. Navigate to the Related Assets tab to see the Process assets where it’s used or open the lineage graph for any downstream asset.
3. Click the Process node representing the executed query.
4. In the sidebar, open the Relations tab to view contributing UDFs and Stored Procedures.

Make sure your Snowflake connector has the required metadata access and that lineage extraction is enabled.

📘 Refer to the Snowflake setup guide for required permissions and supported lineage scenarios.

New  Workflows Assets 

🎉 What’s new

Atlan now supports a native Iceberg connector to crawl metadata from your Iceberg lakehouse into Atlan for discovery, governance, and AI-ready context.

The connector is now on Public Preview and integrates with:

• All Iceberg REST-spec-compliant catalogs, including Apache Polaris, Snowflake Open Catalog, Databricks Unity Catalog (Iceberg REST), Apache Gravitino, Project Nessie, Lakekeeper and more. Supports OAuth2 client credentials for auth.
• BigLake Metastore (BLM), with multiple GCP-based auth modes supported: Service Account Key auth and Workload Identity Federation (WIF)

✨ Let’s dig deeper

The Iceberg connector helps teams operationalize Iceberg metadata in Atlan by enabling you to:

• Ingest core Iceberg assets across catalogs, namespaces, tables and columns (including nested columns)
• Surface key table metadata such as record counts, size, and snapshot details (where available)
• Make Iceberg data easier to find and trust through Atlan’s governance, ownership, and discovery experiences
• Provide richer metadata context to downstream AI and analytics workflows, improving AI-assisted discovery and usability

With this launch, teams can bring Iceberg metadata into a single governed context layer and make lakehouse assets far easier to discover, understand, and use at scale.

👏 Give it a shot

🎉 What’s new

As a data leader, you'll want to understand how your teams are adopting and using Atlan.

We've added a new usage_analytics namespace/schema to Atlan Lakehouse that exposes product telemetry captured from Atlan’s UI – every page view, user action, and identity snapshot – so you can understand how your teams interact with Atlan and measure adoption, engagement, retention and beyond.

Previously, you had to rely on custom charts and exports to understand who was active, which features were used, and how engagement changed over time. Now, curated product usage tables live directly in your Lakehouse, so you can analyze product usage using the same SQL and tools you already use and trust.

✨ Let’s dig deeper

In practice, this means you can turn raw Atlan activity into clear, visual insights about how your teams work with data:

• Track active users over time by workspace, role, or team so you can see whether onboarding and enablement actually stick.
• Understand feature adoption by looking at which core workflows (like search, lineage, glossary, or queries) are used most, and where usage drops off.
• Monitor engagement patterns (daily, weekly, monthly activity) to spot healthy teams, identify dormant groups, and time interventions before adoption slips.
• Build retention views that show how frequently users come back after their first week or month, and which behaviors correlate with long-term value.
• Join usage analytics with your own business data (for example, teams, departments, or customers) so you can see which parts of the organization get the most value from Atlan.

Because everything lives in your Lakehouse, you can plug these tables into your favorite compute platform or data visualization tool, so you can build reports and AI applications to understand how Atlan usage evolves across teams, features, and time.

👏 Give it a shot

You can start exploring usage analytics data for your Atlan tenant today:

• First, make sure Lakehouse is enabled for your Atlan tenant
• In your Lakehouse environment, look for the usage_analytics namespace/schema alongside your existing Atlan metadata schemas
  • Use your preferred compute platform to run SQL queries against the usage_analytics tables to inspect active users, features, or time ranges
  • Connect your BI or reporting tool to your compute engine to build dashboards that visualize active users, feature adoption, and retention for your Atlan tenant
  • If you use Claude as your AI assistant, run the same usage analytics queries conversationally using the atlan-usage-analytics skills repository so you can ask questions in natural language instead of writing SQL.
• Check out sample queries for product usage analytics in our use case documentation.

📘 Learn more

Visit the docs to learn more about the usage_analytics schema, including tables, use cases, and sample queries you can use to drive product usage analytics use cases.

AssetsWorkflows
🎉 What’s new

Integrate Atlan with Google BigQuery based on Workload Identity Federation authentication.

Previously, connecting Atlan to BigQuery required generating, downloading, and manually rotating static service account keys—a process that created potential security vulnerabilities and maintenance debt. Now, you can establish a "keyless" trust relationship between Atlan and Google Cloud, allowing for secure, short-lived authentication that adheres to zero-trust security principles.

✨ Let’s dig deeper

• Use Atlan’s OIDC provider to issue identity-based tokens that are automatically exchanged for temporary GCP credentials.
• Eliminate the need for manual key rotation or storage of sensitive JSON files.
• Scope permissions specifically to designated service accounts using GCP’s IAM impersonation.

👏 Give it a shot

1. Establish trust between your GCP account and Atlan's OIDC provider by following these steps.
2. Navigate to your Atlan BigQuery workflow settings and select Workload Identity Federation as the authentication method. More details here.

Workflows New 

🎉 What’s new

We’ve launched a new connector for MongoDB (self-managed/on-prem) in Public Preview. You can now securely scan metadata from MongoDB on-prem and ingest into Atlan.

This connector complements the existing MongoDB Atlas connector which can be used for integrating with the Saas of MongoDB.

✨ Let’s dig deeper

• Connect your MongoDB on-prem (Community/Enterprise edition) instance to Atlan and ingest database, collections and field information
• Field Inference - The connector reads sample documents and infers field information.
• Nested Fields - Nested fields are also derived and created as first-class assets
• Execution Modes - Deploy the connector using Self-Deployed Runtime approach based on requirements

👏 Give it a shot

A step-by-step setup guide is available as part of the MongoDB(self-managed) connector guide

🌊 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!