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USAGE_ANALYTICS namespace

TL;DR

Reference for Segment-derived usage analytics tables available in the Lakehouse

Connect

The USAGE_ANALYTICS namespace exposes product telemetry captured from Atlan's web application, including page views, user actions, and identity snapshots. It also captures usage of Atlan's AI interfaces—MCP, Conversational AI, Slack, and Teams. Use it to understand how users interact with Atlan across discovery, governance, lineage, and AI features, and to measure adoption, engagement, and retention for a customer domain.

Data freshness

Usage analytics data is refreshed once per day. Queries reflect activity up to the most recent daily refresh, not real-time events.

Core tables​

  • PAGES: Records every page view in Atlan. Contains the page name, tab, asset GUID, connector name, asset type, user ID, customer domain, and timestamp. Use this table to understand navigation patterns, asset browsing behavior, and connector coverage.
  • TRACKS: Records every discrete user action in Atlan, including searches, clicks, saves, governance edits, and AI interactions. Contains the event name, user ID, and timestamp. Use this table to measure feature engagement and specific interaction patterns.
  • USERS: Identity snapshots for Atlan users. Contains user ID, email, username, role, license type, job role, and account creation timestamp. Use this table to enrich activity data with user metadata.
  • AI_TOOL_CALLS: Records one row per tool call across Atlan's AI interfaces—MCP, Conversational AI, Slack, and Teams. Contains the interface, tool name, user ID, timestamp, duration, error flag, and customer domain, plus the tool input and output. MCP records also include the client name (for example, Claude, Cursor, Codex, or VS Code) and authentication type (human session or service account). Use this table to measure which AI interfaces, clients, and tools are used most, and where calls fail or run slowly.
  • AI_TURNS: Records one row per user message in Conversational AI, Slack, and Teams. Contains the turn ID, conversation ID, interface, user ID, timestamp, duration, error flag, the user's question, and the customer domain. Use this table to measure questions asked, conversation length, follow-up behavior, and response times. MCP doesn't populate this table—it has no turn or conversation concept.
Coverage varies by AI interface
  • The interface column identifies the surface: mcp, conversational_ai, slack, or teams.
  • AI_TURNS covers Conversational AI, Slack, and Teams. MCP activity is captured in AI_TOOL_CALLS only.
  • client_name and auth_type are populated for MCP records.
  • conversation_id is NULL for Teams (and for MCP rows in AI_TOOL_CALLS).
  • To relate the two tables, join AI_TOOL_CALLS.turn_id to AI_TURNS.turn_id. Use a LEFT JOIN for cross-interface queries—MCP rows have a NULL turn_id, so an inner join drops all MCP traffic.

Known limitations​

USERS.created_at may be NULL

The created_at column in USAGE_ANALYTICS.USERS is currently NULL for users provisioned via SSO, SCIM, or admin invite on recent platform versions. Don't use tenant_created_at as a substitute—it represents your tenant's deployment date and is the same value for every user in your tenant.

As a workaround, use either of the following as a proxy for a user's first appearance:

  • RECEIVED_AT from USAGE_ANALYTICS.USERS: the timestamp Segment received the identity record. Closest single-column substitute for created_at.
  • First activity timestamp derived from PAGES and TRACKS: take MIN(timestamp) for a user across both tables to get the earliest tracked event. The User roster query already returns this as the first_activity column.

Example queries​

Use these pages for ready-to-run queries based on PAGES, TRACKS, and USERS:

  • Track active users: DAU, WAU, MAU, stickiness ratio, and user roster.
  • Analyze feature adoption: Connector usage, adoption matrix, engagement quadrant, weekly trends, top events, and top pages.
  • Assess engagement depth: Pageviews per user, actions per session, session duration, engagement tiers, and power users.
  • Measure retention: Activation funnel, churned users, reactivated users, day-N retention, funnel analysis, and monthly cohort retention.

See also​

  • Run usage analytics queries with Claude skills: Use pre-built Claude skills to run common usage analytics queries conversationally. This can help when you want results without writing SQL.
  • Use cases: Browse Lakehouse use cases across metadata quality, lineage, glossary analysis, and usage analytics. Each use case includes ready-to-run example queries.