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Introduces an analytics/ layer that wraps production Postgres data in safe, read-only views exposed under the analytics schema. - 14 documented query files in queries/ (one per Looker data source) covering auth activities, user activity, execution metrics, onboarding funnel, and cohort retention (login + execution, weekly + daily) - setup.sql — one-time schema creation and role/grant setup for the analytics_readonly role (auth, platform, analytics schemas) - generate_views.py — reads queries/*.sql and applies CREATE OR REPLACE VIEW analytics.<name> to the database; supports --dry-run, --only, and --db-url flags - views.sql — pre-generated combined reference output - README.md — full setup, deployment, and integration guide Looker, PostHog Data Warehouse, and Supabase MCP (for Otto) all connect to the same analytics.* views instead of raw tables.
46 lines
1.8 KiB
SQL
46 lines
1.8 KiB
SQL
-- =============================================================
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-- View: analytics.user_onboarding
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-- Looker source alias: ds68 | Charts: 3
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-- =============================================================
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-- DESCRIPTION
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-- One row per user onboarding record. Contains the user's
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-- stated usage reason, selected integrations, completed
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-- onboarding steps and optional first agent selection.
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-- Full history (no date filter) since onboarding happens
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-- once per user.
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--
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-- SOURCE TABLES
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-- platform.UserOnboarding — Onboarding state per user
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--
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-- OUTPUT COLUMNS
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-- id TEXT Onboarding record UUID
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-- createdAt TIMESTAMPTZ When onboarding started
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-- updatedAt TIMESTAMPTZ Last update to onboarding state
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-- usageReason TEXT Why user signed up (e.g. 'work', 'personal')
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-- integrations TEXT[] Array of integration names the user selected
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-- userId TEXT User UUID
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-- completedSteps TEXT[] Array of onboarding step enums completed
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-- selectedStoreListingVersionId TEXT First marketplace agent the user chose (if any)
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--
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-- EXAMPLE QUERIES
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-- -- Usage reason breakdown
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-- SELECT "usageReason", COUNT(*) FROM analytics.user_onboarding GROUP BY 1;
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--
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-- -- Completion rate per step
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-- SELECT step, COUNT(*) AS users_completed
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-- FROM analytics.user_onboarding
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-- CROSS JOIN LATERAL UNNEST("completedSteps") AS step
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-- GROUP BY 1 ORDER BY users_completed DESC;
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-- =============================================================
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SELECT
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id,
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"createdAt",
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"updatedAt",
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"usageReason",
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integrations,
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"userId",
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"completedSteps",
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"selectedStoreListingVersionId"
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FROM platform."UserOnboarding"
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