🎉 What's new

Atlan Lakehouse just got faster, more data and more reliable. Anything you need out of Atlan, from AI usage data to asset metadata to full exports, is available from the same open, governed layer, and is performant enough to build any application on. 

📊 Track how your teams use Atlan's AI interfaces

Atlan offers several conversational AI interfaces, so your teams can work with their enterprise context layer from any tool they want, whether it's their own agentic tools via Atlan MCP, Slack and Teams, or Atlan's built-in Conversational AI chat. You can now easily track how your teams use those interfaces directly in your own Atlan Lakehouse with two new tables:

The USAGE_ANALYTICS namespace in your Lakehouse now has two AI usage tables, refreshed daily: AI_TOOL_CALLS (one row per tool call across all of Atlan's AI interfaces) and AI_TURNS (one row per user question in Conversational AI, Slack, and Teams). Both sit alongside the metadata and product usage data already in your Lakehouse.

• Identify active AI users: Count questions and tool calls, and see your most active AI users by joining the new AI usage tables with USAGE_ANALYTICS.USERS.
• See which tools and clients lead: Use the client_name column to see which clients your teams use (e.g., Claude, Cursor, Codex, VS Code), and the tool_name column to see which Atlan capabilities they call most.
• Measure conversation depth: Group AI_TURNS by conversation_id ordered by timestamp to separate new questions from follow-ups, and understand how your teams' conversations evolve.
  
• Find friction fast: Use the is_error and duration_ms fields to see where AI calls fail or make people wait.

📊 Meanwhile, your asset exports just got dramatically faster

All asset export workflows (advanced, basic, admin) now run on a shared backend built on Atlan Lakehouse. It’s same UI, same configuration, same output but built on a faster, more scalable foundation. Exports that used to take more than a day now complete in less than half an hour, moving over a million assets. The switchover is behind the scenes – no changes needed from you.

👏 Get all the benefits of Atlan Lakehouse today

Atlan Lakehouse is generally available for all Atlan tenants.

• Use any engine: Pick up configuration details from Admin/Settings → Lakehouse (admin users only), and point any Iceberg REST-compatible engine at it, e.g., Snowflake, Trino, DuckDB.
• Use any AI tool: Install the /atlan-lakehouse skill to your favorite AI tool (e.g., Claude Code) and use natural language to ask any questions about your Atlan asset metadata and usage data – no query engine required.

And what might you want to access?

• Usage analytics: The USAGE_ANALYTICS namespace exposes page views, user actions, and adoption data you can analyze directly, with MCP and conversational AI logs now available.
• Relationship analytics: Metadata for all of your Atlan assets are in the Lakehouse, including user-defined relationships with full edge semantics, meaning your business-graph relationships are queryable for both your AI agents and analytical use cases, not just linked-asset lookups.

💥  More on the way!

Lakehouse already works with engines such as Snowflake, Databricks, BigQuery, Trino, and DuckDB. We're also building Atlan-managed Lakehouse Compute for when you want to query your data without running the compute layer yourself. More details to come!