TL;DR

Use Lakehouse to measure daily, weekly, and monthly active users from the usage_analytics namespace.

Your AI can read this via Docs MCPcurl -fsSL "https://docs.atlan.com/install-docs-mcp" | bashConnect

Track active users

You can use Lakehouse to track daily, weekly, and monthly active users for a customer domain and monitor adoption over time. This page provides example queries for active-user metrics in the USAGE_ANALYTICS schema, including DAU, WAU, MAU, stickiness ratio, and a user roster.

note

If your Atlan users block tracking cookies or requests (for example, using Privacy Badger, ad blockers, or browser privacy settings), these metrics may be understated.

Query parameters

These queries use the tables in the USAGE_ANALYTICS schema. For table definitions, see USAGE_ANALYTICS namespace. Replace the placeholders in each query with values for your environment.

{{DATABASE}}: Snowflake database name
{{SCHEMA}}: Use USAGE_ANALYTICS
{{DOMAIN}}: Customer domain, for example 'acme.atlan.com'
{{START_DATE}}: Start date, for example '2025-01-01'

Daily active users

Returns the count of distinct users who performed any tracked action or page view on each calendar day for the specified domain.

Snowflake

-- dau_by_domain.sql
-- Daily Active Users per customer domain.
-- Uses PAGES.domain for filtering. Counts by user_id.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com' (or replace filter line with 1=1 for all)

WITH user_domains AS (
    SELECT user_id, MAX(domain) AS domain
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES
    WHERE domain IS NOT NULL
    GROUP BY user_id
),

activity_events AS (
    SELECT
        t.user_id,
        ud.domain,
        DATE(CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', t.TIMESTAMP)) AS event_date
    FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
    INNER JOIN user_domains ud ON ud.user_id = t.user_id
    WHERE t.event_text NOT IN (
        'atlan_analaytics_aggregateinfo_fetch',
        'api_error_emit', 'api_evaluator_cancelled', 'api_evaluator_succeeded',
        'Experiment Started', '$experiment_started',
        'web_vital_metric_inp_track', 'web_vital_metric_ttfb_track',
        'performance_metric_user_timing_discovery_search',
        'performance_metric_user_timing_app_bootstrap',
        'web_vital_metric_fcp_track', 'web_vital_metric_lcp_track'
    )
    AND t.event_text NOT LIKE 'workflow_%'
    AND t.TIMESTAMP >= {{START_DATE}}

    UNION ALL

    SELECT
        p.user_id,
        p.domain,
        DATE(CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', p.TIMESTAMP)) AS event_date
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
    WHERE p.TIMESTAMP >= {{START_DATE}}
)

SELECT
    domain,
    event_date,
    COUNT(DISTINCT user_id) AS dau
FROM activity_events
WHERE domain = {{DOMAIN}}
GROUP BY domain, event_date
ORDER BY domain, event_date DESC;

Sample output

DOMAIN	EVENT_DATE	DAU
acme.atlan.com	2025-03-15	47
acme.atlan.com	2025-03-14	52
acme.atlan.com	2025-03-13	38

Weekly active users

Returns the count of distinct users who performed any tracked action or page view within each calendar week for the specified domain.

Snowflake

-- wau_by_domain.sql
-- Weekly Active Users per customer domain.
-- Uses PAGES.domain for filtering. Counts by user_id.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com' (or replace filter line with 1=1 for all)

WITH user_domains AS (
    SELECT user_id, MAX(domain) AS domain
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES
    WHERE domain IS NOT NULL
    GROUP BY user_id
),

activity_events AS (
    SELECT
        t.user_id,
        ud.domain,
        DATE_TRUNC('WEEK', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', t.TIMESTAMP)) AS event_week
    FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
    INNER JOIN user_domains ud ON ud.user_id = t.user_id
    WHERE t.event_text NOT IN (
        'atlan_analaytics_aggregateinfo_fetch',
        'api_error_emit', 'api_evaluator_cancelled', 'api_evaluator_succeeded',
        'Experiment Started', '$experiment_started',
        'web_vital_metric_inp_track', 'web_vital_metric_ttfb_track',
        'performance_metric_user_timing_discovery_search',
        'performance_metric_user_timing_app_bootstrap',
        'web_vital_metric_fcp_track', 'web_vital_metric_lcp_track'
    )
    AND t.event_text NOT LIKE 'workflow_%'
    AND t.TIMESTAMP >= {{START_DATE}}

    UNION ALL

    SELECT
        p.user_id,
        p.domain,
        DATE_TRUNC('WEEK', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', p.TIMESTAMP)) AS event_week
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
    WHERE p.TIMESTAMP >= {{START_DATE}}
)

SELECT
    domain,
    event_week,
    COUNT(DISTINCT user_id) AS wau
FROM activity_events
WHERE domain = {{DOMAIN}}
GROUP BY domain, event_week
ORDER BY domain, event_week DESC;

Sample output

DOMAIN	EVENT_WEEK	WAU
acme.atlan.com	2025-03-10	89
acme.atlan.com	2025-03-03	94
acme.atlan.com	2025-02-24	81

Monthly active users

Returns the count of distinct users active each calendar month with month-over-month delta and percentage change.

Snowflake

-- mau_by_domain.sql
-- Monthly Active Users per customer domain with month-over-month delta.
-- Uses PAGES.domain for domain filtering. Counts by user_id (not email).
-- LEFT JOINs USERS for optional email enrichment.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com' (or replace filter line with 1=1 for all)

WITH user_domains AS (
    SELECT user_id, MAX(domain) AS domain
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES
    WHERE domain IS NOT NULL
    GROUP BY user_id
),

activity_events AS (
    SELECT
        t.user_id,
        ud.domain,
        DATE_TRUNC('MONTH', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', t.TIMESTAMP)) AS event_month
    FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
    INNER JOIN user_domains ud ON ud.user_id = t.user_id
    WHERE t.event_text NOT IN (
        'atlan_analaytics_aggregateinfo_fetch',
        'api_error_emit', 'api_evaluator_cancelled', 'api_evaluator_succeeded',
        'Experiment Started', '$experiment_started',
        'web_vital_metric_inp_track', 'web_vital_metric_ttfb_track',
        'performance_metric_user_timing_discovery_search',
        'performance_metric_user_timing_app_bootstrap',
        'web_vital_metric_fcp_track', 'web_vital_metric_lcp_track'
    )
    AND t.event_text NOT LIKE 'workflow_%'
    AND t.TIMESTAMP >= {{START_DATE}}

    UNION ALL

    SELECT
        p.user_id,
        p.domain,
        DATE_TRUNC('MONTH', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', p.TIMESTAMP)) AS event_month
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
    WHERE p.TIMESTAMP >= {{START_DATE}}
),

mau_counts AS (
    SELECT
        domain,
        event_month,
        COUNT(DISTINCT user_id) AS mau
    FROM activity_events
    WHERE domain = {{DOMAIN}}
    GROUP BY domain, event_month
)

SELECT
    domain,
    event_month,
    mau,
    LAG(mau) OVER (PARTITION BY domain ORDER BY event_month) AS prev_month_mau,
    mau - LAG(mau) OVER (PARTITION BY domain ORDER BY event_month) AS mau_delta,
    ROUND(100.0 * (mau - LAG(mau) OVER (PARTITION BY domain ORDER BY event_month))
        / NULLIF(LAG(mau) OVER (PARTITION BY domain ORDER BY event_month), 0), 1) AS mau_change_pct
FROM mau_counts
ORDER BY domain, event_month DESC;

Sample output

DOMAIN	EVENT_MONTH	MAU	PREV_MONTH_MAU	MAU_DELTA	MAU_CHANGE_PCT
acme.atlan.com	2025-03-01	124	118	6	5.1
acme.atlan.com	2025-02-01	118	110	8	7.3
acme.atlan.com	2025-01-01	110	null	null	null

Stickiness ratio

Computes the DAU/MAU stickiness ratio per domain per month. A ratio greater than 0.3 indicates strong daily engagement. A ratio less than 0.1 indicates episodic usage.

Snowflake

-- mau_dau_ratio.sql
-- DAU/MAU stickiness ratio per domain per month.
-- >0.3 = strong daily engagement, <0.1 = episodic usage.
-- Uses PAGES.domain for filtering. Counts by user_id.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com' (or replace filter line with 1=1 for all)

WITH user_domains AS (
    SELECT user_id, MAX(domain) AS domain
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES
    WHERE domain IS NOT NULL
    GROUP BY user_id
),

activity_events AS (
    SELECT
        t.user_id,
        ud.domain,
        DATE(CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', t.TIMESTAMP)) AS event_date,
        DATE_TRUNC('MONTH', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', t.TIMESTAMP)) AS event_month
    FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
    INNER JOIN user_domains ud ON ud.user_id = t.user_id
    WHERE t.event_text NOT IN (
        'atlan_analaytics_aggregateinfo_fetch',
        'api_error_emit', 'api_evaluator_cancelled', 'api_evaluator_succeeded',
        'Experiment Started', '$experiment_started',
        'web_vital_metric_inp_track', 'web_vital_metric_ttfb_track',
        'performance_metric_user_timing_discovery_search',
        'performance_metric_user_timing_app_bootstrap',
        'web_vital_metric_fcp_track', 'web_vital_metric_lcp_track'
    )
    AND t.event_text NOT LIKE 'workflow_%'
    AND t.TIMESTAMP >= {{START_DATE}}

    UNION ALL

    SELECT
        p.user_id,
        p.domain,
        DATE(CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', p.TIMESTAMP)) AS event_date,
        DATE_TRUNC('MONTH', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', p.TIMESTAMP)) AS event_month
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
    WHERE p.TIMESTAMP >= {{START_DATE}}
),

daily_users AS (
    SELECT
        domain,
        event_month,
        event_date,
        COUNT(DISTINCT user_id) AS dau
    FROM activity_events
    WHERE domain = {{DOMAIN}}
    GROUP BY domain, event_month, event_date
),

monthly_users AS (
    SELECT
        domain,
        event_month,
        COUNT(DISTINCT user_id) AS mau
    FROM activity_events
    WHERE domain = {{DOMAIN}}
    GROUP BY domain, event_month
)

SELECT
    m.domain,
    m.event_month,
    m.mau,
    ROUND(AVG(d.dau), 1) AS avg_dau,
    ROUND(AVG(d.dau) / NULLIF(m.mau, 0), 3) AS stickiness_ratio,
    CASE
        WHEN AVG(d.dau) / NULLIF(m.mau, 0) >= 0.3 THEN 'Strong'
        WHEN AVG(d.dau) / NULLIF(m.mau, 0) >= 0.1 THEN 'Moderate'
        ELSE 'Episodic'
    END AS engagement_level
FROM monthly_users m
JOIN daily_users d ON d.domain = m.domain AND d.event_month = m.event_month
GROUP BY m.domain, m.event_month, m.mau
ORDER BY m.domain, m.event_month DESC;

Sample output

DOMAIN	EVENT_MONTH	MAU	AVG_DAU	STICKINESS_RATIO	ENGAGEMENT_LEVEL
acme.atlan.com	2025-03-01	124	41.2	0.332	Strong
acme.atlan.com	2025-02-01	118	18.5	0.157	Moderate
acme.atlan.com	2025-01-01	110	8.9	0.081	Episodic

User roster

Returns the full list of active users for a domain with activity metadata: total events, active days, first and last activity timestamps, days since last activity, and a status label (Active, Inactive, or Churned).

Snowflake

-- user_roster_by_domain.sql
-- Full user list for a domain with activity status, last activity, and event counts.
-- Starts from active user_ids in PAGES, LEFT JOINs USERS for metadata.
-- Shows all active users even if they have no USERS record.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com'

WITH user_domains AS (
    SELECT user_id, MAX(domain) AS domain
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES
    WHERE domain IS NOT NULL
    GROUP BY user_id
),

activity_events AS (
    SELECT
        t.user_id,
        CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', t.TIMESTAMP) AS event_ts
    FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
    INNER JOIN user_domains ud ON ud.user_id = t.user_id AND ud.domain = {{DOMAIN}}
    WHERE t.event_text NOT IN (
        'atlan_analaytics_aggregateinfo_fetch',
        'api_error_emit', 'api_evaluator_cancelled', 'api_evaluator_succeeded',
        'Experiment Started', '$experiment_started',
        'web_vital_metric_inp_track', 'web_vital_metric_ttfb_track',
        'performance_metric_user_timing_discovery_search',
        'performance_metric_user_timing_app_bootstrap',
        'web_vital_metric_fcp_track', 'web_vital_metric_lcp_track'
    )
    AND t.event_text NOT LIKE 'workflow_%'
    AND t.TIMESTAMP >= {{START_DATE}}

    UNION ALL

    SELECT
        p.user_id,
        CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', p.TIMESTAMP) AS event_ts
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
    WHERE p.domain = {{DOMAIN}}
      AND p.TIMESTAMP >= {{START_DATE}}
),

user_activity AS (
    SELECT
        user_id,
        COUNT(*) AS total_events,
        COUNT(DISTINCT DATE(event_ts)) AS active_days,
        MIN(event_ts) AS first_activity,
        MAX(event_ts) AS last_activity
    FROM activity_events
    GROUP BY user_id
),

user_meta AS (
    SELECT id, email, username, role,
           MAX(license_type) AS license_type,
           MAX(job_role) AS job_role,
           MIN(created_at) AS user_created_at
    FROM {{DATABASE}}.{{SCHEMA}}.USERS
    WHERE email IS NOT NULL
    GROUP BY id, email, username, role
)

SELECT
    a.user_id,
    um.email,
    um.username,
    um.role,
    um.license_type,
    um.job_role,
    um.user_created_at,
    a.total_events,
    a.active_days,
    a.first_activity,
    a.last_activity,
    DATEDIFF('day', a.last_activity, CURRENT_TIMESTAMP()) AS days_since_last_activity,
    CASE
        WHEN a.last_activity >= DATEADD('day', -30, CURRENT_TIMESTAMP()) THEN 'Active'
        WHEN a.last_activity >= DATEADD('day', -90, CURRENT_TIMESTAMP()) THEN 'Inactive'
        WHEN a.last_activity IS NULL THEN 'Never Active'
        ELSE 'Churned'
    END AS status
FROM user_activity a
LEFT JOIN user_meta um ON um.id = a.user_id
ORDER BY a.total_events DESC;

Sample output

USER_ID	EMAIL	USERNAME	ROLE	LICENSE_TYPE	JOB_ROLE	USER_CREATED_AT	TOTAL_EVENTS	ACTIVE_DAYS	FIRST_ACTIVITY	LAST_ACTIVITY	DAYS_SINCE_LAST_ACTIVITY	STATUS
usr_001	alice@acme.com	alice	admin	guest	Data Engineer	2024-01-10	3420	58	2025-01-02	2025-03-14	1	Active
usr_002	bob@acme.com	bob	member	member	Data Analyst	2024-02-15	1850	41	2025-01-05	2025-03-10	5	Active
usr_003	carol@acme.com	carol	member	guest	Business Analyst	2024-03-01	320	12	2025-01-08	2024-12-20	85	Inactive

Query parameters​

Daily active users​

Weekly active users​

Monthly active users​

Stickiness ratio​

User roster​

Query parameters

Daily active users

Weekly active users

Monthly active users

Stickiness ratio

User roster