🎉 What’s new

The Atlan Lakehouse is the context store underneath your AI agents — the open, Iceberg-native layer where your enterprise's context lives, gets governed, and ships to any agent platform.

We're excited to announce that Atlan Lakehouse is now generally available – the foundation of the Context Layer for AI, enabled by default for all Atlan tenants. You can run SQL queries, create dashboards, and build AI applications directly on your Atlan context, using any Iceberg REST–compatible client, without managing separate pipelines or exports.

✨ Let’s dig deeper

• Make your context queryable by anyone — not just Atlan users. Connect Lakehouse to your existing BI tools – Tableau, Power BI, Looker – and build governance scorecards, domain coverage heatmaps, and enrichment dashboards that update automatically as your catalog changes. No manual data maintenance, no exports, no staleness: the people who need to see governance progress can see it in the tools they already use.
• Decommission your extraction pipelines – your context layer is already running. Teams that previously built custom pipelines on top of Atlan APIs – with multiple components, failure points, and API rate-limit concerns – have migrated to Lakehouse and decommissioned those pipelines entirely. Instead of engineering a context layer from scratch, you get a stable, always-current store you can query directly.
• Score your assets for AI-readiness, in a single query. Lakehouse combines asset-level context — enrichment coverage, data quality scores, certifications, lineage — with Atlan's own usage signals: which users are active, which assets are being queried, which features are driving adoption. Teams have used this to score assets for AI-readiness, track governance progress over time, and understand whether the context their teams are creating is actually being used — all in a single query layer, without stitching together separate exports.

👏 Give it a shot

• Connect to the Atlan Lakehouse from your preferred Iceberg REST–compatible client, explore the context that resides in the Lakehouse, and run a few starter queries (e.g., counting assets by connector, surfacing verified assets, or listing active users).
• Plug Lakehouse into your existing reporting or AI stack – connect your BI tool to build context dashboards, or wire it into your AI agents so they can reason over your catalog, governance, and usage data using SQL instead of custom integrations.

Lakehouse is the foundation. Over the coming weeks, work with your Atlan team and look for more updates here on how Context Engineering Studio, Conversational AI, and MCP connect to it – so you have a full picture of what the Context Layer for AI looks like end to end.

Check out the documentation for more:

• Atlan Lakehouse overview
• Enable agents to query the Atlan Lakehouse using the /atlan-lakehouse agent skill

🎉 What's new 

Put your most important metadata front and center!

With Featured Custom Metadata, you can now pin custom metadata properties to the top of any asset profile, so critical context like governance status, ownership, or compliance info is always visible at a glance.

✨ Let's dig deeper 

Until now, finding a specific custom metadata value meant scrolling through the full details section even if it was the one property you checked every single time. That changes today.

📌 Pin What Matters
Governance Admins and Admins can now toggle Show as Featured on any custom metadata property. Once enabled, that property appears in a dedicated "Featured Custom Metadata" section at the top of every linked asset's profile.

Go to any Custom Metadata group → select a property → enable Show as Featured (right next to the existing Show in Filters and Overview options).

✏️ Edit Inline
Click edit on any featured property and update it directly. The changes sync instantly across the full custom metadata panel and the featured section. No need to navigate elsewhere.

📄 Rich Text Support If your featured property is a rich text field, it renders in an expanded view with the option to open in full, perfect for detailed governance notes or documentation.

📚 Works Across Glossaries Too
Featured Custom Metadata isn't limited to data assets. It's available on glossary categories and glossary terms as well — so business context stays visible wherever your team is working.

🌍 Where does this work? Featured Custom Metadata is supported on:

✅ Tables, columns, and other data assets ✅ Glossary categories ✅ Glossary terms ⏳Coming soon on data products

Why it matters: The metadata that drives decisions lke governance approvals, ownership, compliance status should never be buried. This feature ensures your most important context is always one glance away.

👏 Give it a shot
Head to any custom metadata property, flip on the Show as Featured toggle, and see it appear on your asset profiles instantly.

No more digging through details panels. Your key metadata now lives where it belongs: right at the top ✨

Let us know how you're using featured metadata. We'd love to hear what you're pinning!

🎉 What’s new

The Atlan Lakehouse makes all of your Atlan context available through an open, Iceberg REST–compatible catalog so you can easily build AI applications and drive analytical reports on your data estate. Today, it stores detailed metadata about all assets across your data estate, as well as usage analytics data on how users interact with Atlan.

We're excited to announce a new gold namespace in the Atlan Lakehouse – a curated namespace in the Lakehouse catalog that consists of standardized, refined tables that make it easier than ever to build AI apps and reports on asset metadata.

✨ Let’s dig deeper

• The Lakehouse namespace that contains asset metadata today (entity_metadata) is effectively a bronze layer for Atlan metadata: comprehensive and highly detailed, but harder to work with when you just want to analyze asset metadata.
• The new catalog-native gold namespace gives you a small set of refined, analytics-ready tables with just the fields you need, making it much easier to build AI apps and reports on asset metadata.
• The entity_metadata namespace remains the raw, bronze source of truth for catalog metadata, while the gold namespace is the curated consumption tier you use for reporting, dashboards, and AI.
• The gold namespace follows a star schema design, with:
  • A core assets table (fact table) that lists every asset in the data estate along with common attributes like name, type, status, owners, and certification, and
  • Complementary domain-specific tables (dimension tables) that contain detailed columns for specific areas such as data quality, data products, and glossary analytics.

👏 Give it a shot

Make sure the Atlan Lakehouse is enabled in your workspace, and then connect to it from your preferred Iceberg REST–compatible client. You'll be able to see a new gold namespace alongside the existing namespaces in the catalog!

Check out the documentation for more:

• Atlan Lakehouse overview
• gold namespace reference
• Best practices for querying the gold namespace efficiently
• Enable agents to query the Atlan Lakehouse using the /atlan-lakehouse agent skill

Note: Ensure your users can view and select from the tables in the gold namespace by granting them the correct platform-native RBAC permissions.

🎉 What’s new

As a data steward, you'll want governance coverage for nested fields because it helps you enforce PII and compliance policies across all your data not just top-level columns.

Until now, Atlan only synced tags for top-level (Level 1) BigQuery columns. While nested columns were visible, their Policy/IAM tags were not  leaving governance incomplete.

With this update, Atlan now supports tag ingestion, propagation, and policy enforcement for nested BigQuery columns (Level > 1) with support for nested structures up to 15 levels deep.

✨ Let’s dig deeper

Here’s what this means in practice:

• Tags applied in BigQuery (Policy/IAM) are now ingested for nested fields

• Nested columns can now be used in:

  • Tag propagation workflows
  • Policy creation → Select assets
• Governance workflows now treat nested fields as first-class assets

• You can identify sensitive data in structures like:

  • user.address.zipcode
  • event.payload.device.id

This eliminates gaps where sensitive data was previously hidden in nested JSON and ensures governance rules apply consistently across all levels.

👏 Give it a shot

• Go to Governance → Policies
• Create or edit a policy
• In Select assets, include nested columns from BigQuery
• Use tag propagation to automatically extend governance to nested fields

Make sure:

• Your BigQuery connector is active
• You have appropriate governance permissions

📘Refer to the BigQuery set up guide for nested tags.

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