Analyze feature adoption

You can use Lakehouse to analyze which Atlan features users adopt and how deeply they engage with each area. This page provides queries for feature adoption in the USAGE_ANALYTICS schema, including connector usage, adoption matrices, engagement quadrants, trends, and top events and pages.

Before you begin

Before you run these queries, make sure:

Your Lakehouse setup is complete and Lakehouse is enabled for your organization. If setup isn't complete, see Get started with Lakehouse.

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'

Connector usage

Shows which data source connectors and asset types users interact with, revealing whether a customer uses only one source system or a broader set of integrations.

Snowflake

-- connector_usage.sql
-- Which data source connectors and asset types customers interact with.
-- Reveals if a customer uses only Snowflake or also Tableau, dbt, etc.
--
-- Pattern: Schema-aware (domain from PAGES directly, user_id as identity key).
-- PAGES has domain natively so no USERS join is needed.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com' (or replace filter with 1=1 for all)

SELECT
    p.domain,
    p.connector_name,
    p.type_name AS asset_type,
    COUNT(*) AS interactions,
    COUNT(DISTINCT p.user_id) AS unique_users,
    COUNT(DISTINCT p.asset_guid) AS unique_assets_viewed
FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
WHERE p.TIMESTAMP >= {{START_DATE}}
  AND p.connector_name IS NOT NULL
  AND p.domain IS NOT NULL
  AND p.domain = {{DOMAIN}}
GROUP BY p.domain, p.connector_name, p.type_name
ORDER BY interactions DESC;

Sample output

DOMAIN	CONNECTOR_NAME	ASSET_TYPE	INTERACTIONS	UNIQUE_USERS	UNIQUE_ASSETS_VIEWED
acme.atlan.com	snowflake	Table	8420	87	1240
acme.atlan.com	tableau	TableauDashboard	2310	54	187
acme.atlan.com	dbt	dbtModel	1180	31	412

Feature adoption matrix

Produces a per-user, per-month boolean matrix showing which feature areas each user accessed. Combines page visits and tracked events into logical feature categories such as Discovery, Governance, AI Copilot, and Lineage.

Snowflake

-- feature_adoption_matrix.sql
-- Feature-by-user boolean matrix per month.
-- Maps raw page names and events into logical feature areas.
--
-- Pattern: Schema-aware (domain from PAGES directly, user_id as identity key).
-- LEFT JOINs to USERS only for optional metadata (email, username, role).
--
-- Feature area mapping (derived from data exploration):
--   Discovery/Search: pages=discovery, events=discovery_search_*, discovery_filter_*, discovery_asset_*
--   Chrome Extension:  pages=reverse-metadata-sidebar, events=chrome_reverse_*
--   Insights/SQL:      pages=saved_query, events=insights_*
--   Governance:        pages=glossary/term/category/classifications, events=governance_*, gtc_tree_*
--   AI Copilot:        events=atlan_ai_*
--   Lineage:           events=lineage_*
--   Asset Profile:     pages=asset_profile/overview
--   Admin:             pages=users/personas/config/sso/api-access/api_keys/policyManager
--   Workflows:         pages=workflows-home/workflows-profile/runs
--   Data Quality:      pages=monitor
--
-- 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
),

page_features AS (
    SELECT
        p.user_id,
        p.domain,
        DATE_TRUNC('MONTH', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', p.TIMESTAMP)) AS activity_month,
        CASE
            WHEN p.name = 'discovery' THEN 'Discovery'
            WHEN p.name = 'reverse-metadata-sidebar' THEN 'Chrome Extension'
            WHEN p.name = 'saved_query' THEN 'Insights/SQL'
            WHEN p.name IN ('glossary', 'term', 'category', 'classifications', 'custom_metadata') THEN 'Governance'
            WHEN p.name IN ('asset_profile', 'overview') THEN 'Asset Profile'
            WHEN p.name IN ('users', 'personas', 'config', 'sso', 'api-access', 'api_keys', 'policyManager', 'manage') THEN 'Admin'
            WHEN p.name IN ('workflows-home', 'workflows-profile', 'runs', 'playbook') THEN 'Workflows'
            WHEN p.name = 'monitor' THEN 'Data Quality'
            WHEN p.name = 'home' THEN 'Home'
            WHEN p.name = 'marketplace' THEN 'Marketplace'
            WHEN p.name = 'insights' THEN 'Insights/SQL'
            WHEN p.name = 'inbox' THEN 'Notifications'
            WHEN p.name = 'usage' THEN 'Usage Analytics'
            ELSE 'Other'
        END AS feature_area
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
    WHERE p.TIMESTAMP >= {{START_DATE}} AND p.name IS NOT NULL
),

event_features AS (
    SELECT
        t.user_id,
        ud.domain,
        DATE_TRUNC('MONTH', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', t.TIMESTAMP)) AS activity_month,
        CASE
            WHEN t.event_text LIKE 'discovery_%' THEN 'Discovery'
            WHEN t.event_text LIKE 'chrome_%' THEN 'Chrome Extension'
            WHEN t.event_text LIKE 'insights_%' THEN 'Insights/SQL'
            WHEN t.event_text LIKE 'governance_%' OR t.event_text LIKE 'gtc_tree_%' THEN 'Governance'
            WHEN t.event_text LIKE 'atlan_ai_%' THEN 'AI Copilot'
            WHEN t.event_text LIKE 'lineage_%' THEN 'Lineage'
            WHEN t.event_text LIKE 'products_home_%' THEN 'Data Products'
            ELSE NULL
        END AS feature_area
    FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
    INNER JOIN user_domains ud ON ud.user_id = t.user_id
    WHERE t.TIMESTAMP >= {{START_DATE}}
      AND 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_%'
),

all_features AS (
    SELECT user_id, domain, activity_month, feature_area FROM page_features WHERE feature_area != 'Other'
    UNION
    SELECT user_id, domain, activity_month, feature_area FROM event_features WHERE feature_area IS NOT NULL
)

SELECT
    af.domain,
    af.activity_month,
    af.user_id,
    um.email,
    um.username,
    um.role,
    MAX(CASE WHEN af.feature_area = 'Discovery' THEN 1 ELSE 0 END) AS used_discovery,
    MAX(CASE WHEN af.feature_area = 'Chrome Extension' THEN 1 ELSE 0 END) AS used_chrome_ext,
    MAX(CASE WHEN af.feature_area = 'Insights/SQL' THEN 1 ELSE 0 END) AS used_insights,
    MAX(CASE WHEN af.feature_area = 'Governance' THEN 1 ELSE 0 END) AS used_governance,
    MAX(CASE WHEN af.feature_area = 'AI Copilot' THEN 1 ELSE 0 END) AS used_ai_copilot,
    MAX(CASE WHEN af.feature_area = 'Lineage' THEN 1 ELSE 0 END) AS used_lineage,
    MAX(CASE WHEN af.feature_area = 'Asset Profile' THEN 1 ELSE 0 END) AS used_asset_profile,
    MAX(CASE WHEN af.feature_area = 'Admin' THEN 1 ELSE 0 END) AS used_admin,
    MAX(CASE WHEN af.feature_area = 'Workflows' THEN 1 ELSE 0 END) AS used_workflows,
    MAX(CASE WHEN af.feature_area = 'Data Quality' THEN 1 ELSE 0 END) AS used_data_quality,
    MAX(CASE WHEN af.feature_area = 'Data Products' THEN 1 ELSE 0 END) AS used_data_products,
    MAX(CASE WHEN af.feature_area = 'Marketplace' THEN 1 ELSE 0 END) AS used_marketplace
FROM all_features af
LEFT JOIN (
    SELECT id, email, username, role, MAX(job_role) AS job_role
    FROM {{DATABASE}}.{{SCHEMA}}.USERS
    WHERE email IS NOT NULL
    GROUP BY id, email, username, role
) um ON um.id = af.user_id
WHERE af.domain = {{DOMAIN}}
GROUP BY af.domain, af.activity_month, af.user_id, um.email, um.username, um.role
ORDER BY af.activity_month DESC, af.user_id;

Sample output

DOMAIN	ACTIVITY_MONTH	USER_ID	EMAIL	USERNAME	ROLE	USED_DISCOVERY	USED_CHROME_EXT	USED_INSIGHTS	USED_GOVERNANCE	USED_AI_COPILOT	USED_LINEAGE	USED_ASSET_PROFILE	USED_ADMIN	USED_WORKFLOWS	USED_DATA_QUALITY	USED_DATA_PRODUCTS	USED_MARKETPLACE
acme.atlan.com	2025-03-01	usr_001	[email protected]	alice	admin	1	0	1	1	1	1	1	1	0	0	0	0
acme.atlan.com	2025-03-01	usr_002	[email protected]	bob	member	1	1	0	0	0	0	1	0	0	0	0	0

Feature engagement quadrant

Plots each feature area by reach (unique users) versus depth (average events per user), inspired by Heap's engagement matrix. Helps identify which features are broadly adopted but shallow versus niche but deeply engaged.

Snowflake

-- feature_engagement_quadrant.sql
-- Feature engagement quadrant: plots each feature by reach (unique users)
-- vs depth (avg events per user). Inspired by Heap's engagement matrix.
-- Combines PAGES and TRACKS into unified feature categories.
--
-- Pattern: Schema-aware. PAGES has domain natively; TRACKS uses user_domains CTE.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com' (or replace filter 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
),

page_events AS (
    SELECT p.user_id, CASE
            WHEN p.name = 'discovery' THEN 'Discovery/Search'
            WHEN p.name = 'reverse-metadata-sidebar' THEN 'Chrome Extension'
            WHEN p.name IN ('saved_query', 'insights') THEN 'Insights/SQL'
            WHEN p.name IN ('glossary', 'term', 'category', 'classifications', 'custom_metadata') THEN 'Governance'
            WHEN p.name IN ('asset_profile', 'overview') THEN 'Asset Profile'
            WHEN p.name = 'monitor' THEN 'Data Quality'
            WHEN p.name IN ('workflows-home', 'workflows-profile', 'runs', 'playbook') THEN 'Workflows'
            WHEN p.name = 'home' THEN 'Home'
            ELSE NULL END AS feature
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
    WHERE p.domain = {{DOMAIN}}
      AND p.TIMESTAMP >= {{START_DATE}}
      AND p.name IS NOT NULL
),

track_events AS (
    SELECT t.user_id, CASE
            WHEN t.event_text LIKE 'discovery_search%' THEN 'Discovery/Search'
            WHEN t.event_text LIKE 'chrome_%' THEN 'Chrome Extension'
            WHEN t.event_text LIKE 'insights_%' THEN 'Insights/SQL'
            WHEN t.event_text LIKE 'governance_%' OR t.event_text LIKE 'gtc_tree_%' THEN 'Governance'
            WHEN t.event_text LIKE 'atlan_ai_%' THEN 'AI Copilot'
            WHEN t.event_text LIKE 'lineage_%' THEN 'Lineage'
            WHEN t.event_text LIKE 'discovery_metadata_%' THEN 'Metadata Curation'
            WHEN t.event_text = 'main_navigation_button_clicked' THEN 'Navigation'
            ELSE NULL END AS feature
    FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
    INNER JOIN user_domains ud ON ud.user_id = t.user_id AND ud.domain = {{DOMAIN}}
    WHERE t.TIMESTAMP >= {{START_DATE}}
      AND 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_%'
),

combined AS (
    SELECT user_id, feature FROM page_events WHERE feature IS NOT NULL
    UNION ALL
    SELECT user_id, feature FROM track_events WHERE feature IS NOT NULL
),

per_user AS (
    SELECT feature, user_id, COUNT(*) AS events
    FROM combined
    GROUP BY feature, user_id
)

SELECT
    feature,
    COUNT(DISTINCT user_id) AS unique_users,
    SUM(events) AS total_events,
    ROUND(AVG(events), 1) AS avg_events_per_user,
    ROUND(MEDIAN(events), 1) AS median_events_per_user
FROM per_user
GROUP BY feature
ORDER BY unique_users DESC;

Sample output

FEATURE	UNIQUE_USERS	TOTAL_EVENTS	AVG_EVENTS_PER_USER	MEDIAN_EVENTS_PER_USER
Discovery/Search	112	18420	164.5	87.0
Asset Profile	98	9830	100.3	52.0
Governance	43	7210	167.7	120.0
AI Copilot	28	3640	130.0	95.5
Lineage	21	1870	89.0	44.0

Feature trends (weekly)

Shows week-over-week unique users and total events per feature area, enabling you to spot adoption trends, seasonal patterns, or engagement drops for specific features.

Snowflake

-- feature_trend_weekly.sql
-- Week-over-week feature usage trending for a domain.
-- Shows unique users per feature area per week.
--
-- Pattern: Schema-aware (domain from PAGES directly, user_id as identity key).
-- PAGES provides domain natively; TRACKS gets domain via user_domains CTE.
--
-- 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
),

all_activity AS (
    SELECT
        user_id,
        domain,
        DATE_TRUNC('WEEK', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', TIMESTAMP)) AS event_week,
        CASE
            WHEN name = 'discovery' THEN 'Discovery'
            WHEN name = 'reverse-metadata-sidebar' THEN 'Chrome Extension'
            WHEN name IN ('saved_query', 'insights') THEN 'Insights/SQL'
            WHEN name IN ('glossary', 'term', 'category', 'classifications', 'custom_metadata') THEN 'Governance'
            WHEN name IN ('asset_profile', 'overview') THEN 'Asset Profile'
            WHEN name = 'monitor' THEN 'Data Quality'
            WHEN name = 'home' THEN 'Home'
            ELSE NULL
        END AS feature_area
    FROM {{DATABASE}}.{{SCHEMA}}.PAGES
    WHERE TIMESTAMP >= {{START_DATE}} AND name IS NOT NULL

    UNION ALL

    SELECT
        t.user_id,
        ud.domain,
        DATE_TRUNC('WEEK', CONVERT_TIMEZONE('UTC', 'Asia/Kolkata', t.TIMESTAMP)) AS event_week,
        CASE
            WHEN t.event_text LIKE 'discovery_%' THEN 'Discovery'
            WHEN t.event_text LIKE 'chrome_%' THEN 'Chrome Extension'
            WHEN t.event_text LIKE 'insights_%' THEN 'Insights/SQL'
            WHEN t.event_text LIKE 'governance_%' OR t.event_text LIKE 'gtc_tree_%' THEN 'Governance'
            WHEN t.event_text LIKE 'atlan_ai_%' THEN 'AI Copilot'
            WHEN t.event_text LIKE 'lineage_%' THEN 'Lineage'
            ELSE NULL
        END AS feature_area
    FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
    INNER JOIN user_domains ud ON ud.user_id = t.user_id
    WHERE t.TIMESTAMP >= {{START_DATE}}
      AND 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_%'
)

SELECT
    event_week,
    feature_area,
    COUNT(DISTINCT a.user_id) AS unique_users,
    COUNT(*) AS total_events
FROM all_activity a
WHERE a.feature_area IS NOT NULL
  AND a.domain = {{DOMAIN}}
GROUP BY event_week, feature_area
ORDER BY event_week DESC, unique_users DESC;

Sample output

EVENT_WEEK	FEATURE_AREA	UNIQUE_USERS	TOTAL_EVENTS
2025-03-10	Discovery	78	4210
2025-03-10	Asset Profile	65	2180
2025-03-10	Governance	32	1450
2025-03-10	AI Copilot	19	870

Top events

Returns the 50 most frequently fired tracked events for a domain, filtered for noise events, with unique user counts and events-per-user ratios.

Snowflake

-- top_events_by_domain.sql
-- Most frequent tracked events per customer domain (noise filtered).
--
-- Pattern: Schema-aware (domain from PAGES via user_domains CTE, user_id as identity key).
-- TRACKS does not have domain natively, so we derive it from PAGES.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com' (or replace filter 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
)

SELECT
    ud.domain,
    t.event_text,
    COUNT(*) AS event_count,
    COUNT(DISTINCT t.user_id) AS unique_users,
    ROUND(COUNT(*) / NULLIF(COUNT(DISTINCT t.user_id), 0), 1) AS events_per_user
FROM {{DATABASE}}.{{SCHEMA}}.TRACKS t
INNER JOIN user_domains ud ON ud.user_id = t.user_id
WHERE t.TIMESTAMP >= {{START_DATE}}
  AND t.event_text IS NOT NULL
  AND 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 ud.domain = {{DOMAIN}}
GROUP BY ud.domain, t.event_text
ORDER BY event_count DESC
LIMIT 50;

Sample output

DOMAIN	EVENT_TEXT	EVENT_COUNT	UNIQUE_USERS	EVENTS_PER_USER
acme.atlan.com	discovery_search_results	12840	98	131.0
acme.atlan.com	main_navigation_button_clicked	8920	112	79.6
acme.atlan.com	atlan_ai_conversation_prompt_submitted	3410	27	126.3
acme.atlan.com	lineage_tab_clicked	2180	45	48.4

Top pages

Returns the 50 most visited Atlan pages for a domain, with tab-level detail for sub-page analysis and views-per-user ratios.

Snowflake

-- top_pages_by_domain.sql
-- Most visited Atlan pages per customer domain, ranked by usage.
-- Includes tab-level detail for sub-page analysis.
--
-- Pattern: Schema-aware (domain from PAGES directly, user_id as identity key).
-- USERS table is not required since PAGES has domain natively.
--
-- Parameters:
--   {{START_DATE}} - e.g., '2025-01-01'
--   {{DOMAIN}}     - e.g., 'acme.atlan.com' (or replace filter with 1=1 for all)

SELECT
    p.domain,
    p.name AS page_name,
    p.tab,
    COUNT(*) AS page_views,
    COUNT(DISTINCT p.user_id) AS unique_users,
    ROUND(COUNT(*) / NULLIF(COUNT(DISTINCT p.user_id), 0), 1) AS views_per_user
FROM {{DATABASE}}.{{SCHEMA}}.PAGES p
WHERE p.TIMESTAMP >= {{START_DATE}}
  AND p.name IS NOT NULL
  AND p.domain IS NOT NULL
  AND p.domain = {{DOMAIN}}
GROUP BY p.domain, p.name, p.tab
ORDER BY page_views DESC
LIMIT 50;

Sample output

DOMAIN	PAGE_NAME	TAB	PAGE_VIEWS	UNIQUE_USERS	VIEWS_PER_USER
acme.atlan.com	asset_profile	overview	9840	104	94.6
acme.atlan.com	discovery	null	7210	98	73.6
acme.atlan.com	asset_profile	lineage	3180	67	47.5
acme.atlan.com	glossary	null	2410	41	58.8

Before you begin​

Query parameters​

Connector usage​

Feature adoption matrix​

Feature engagement quadrant​

Feature trends (weekly)​

Top events​

Top pages​

Before you begin

Query parameters

Connector usage

Feature adoption matrix

Feature engagement quadrant

Feature trends (weekly)

Top events

Top pages