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Getting started

Access, login, and your first ten minutes in the new BI platform.

Getting started

Access

Tool How you sign in Who has access
Metabase Google Sign-In, restricted to @joe.coffee accounts, at https://joe-metabase.fly.dev brenden, nick, llexi (admins); ryan (Data Analyst); ask an admin to invite others
Reports admin Admin API token (ask David) Ops/analyst use
Raw SQL Metabase’s native SQL editor (recommended), or a read-only Postgres role for external tools Analysts

Metabase sends its own emails (password resets, subscriptions) as reports@joe.coffee; scheduled platform reports come from the same address.

Your first ten minutes

  1. Sign in to Metabase with your @joe.coffee Google account.
  2. Open the Home (Parity) dashboard. It mirrors the Looker Home dashboard: network-level sales, orders, and trend cards.
  3. Browse the parity collection. The dashboards rebuilt from Looker so far:
    • Home (Parity) - network overview, the old Looker landing page
    • Sales Report (Parity) - the per-shop sales workhorse (net sales, tax, tips, discounts, by day)
    • Payout by Arrival Date (Parity) - Stripe payouts by the date they hit the bank
    • All Transactions (Parity) - the transaction-ledger detail view (every order, refund, gift card load, with full money splits)
    • Network Overview / Network Pulse / Store Spotlight - internal network health views
    • Shop Sales Report (Embed) - the shop-locked dashboard used for merchant embeds and PDF reports
  4. Ask a question. Click + New > Question, pick the data-plane database, and start from a bi.* view (not a raw table - see below). The GUI builder does filtering/grouping without SQL.
  5. Or write SQL. + New > SQL query. The same bi.* views are the right entry point.

The one habit that matters

Query the bi.* views, not raw tables, unless you know why you need the raw table. The views encode the metric contract (which rows count, which columns are cents, what “net” means). Raw-table queries that re-derive metrics are how numbers drift apart between surfaces - the exact problem this platform exists to eliminate.

Available views and what they answer are cataloged in the data dictionary.

Data freshness expectations

Data Freshness
Today’s orders (live path: recent_orders, live dashboards) ~20 seconds
Transaction ledger, fact tables, bi.* views Complete through yesterday; nightly sync at 05:20 UTC (9:20pm Pacific) also refreshes a trailing 7-day window to pick up late refunds
Stripe payouts Daily; payouts only arrive on banking days (no weekend rows is normal)

If a closed day’s numbers change slightly a day later, that is the trailing-window resync picking up refunds and adjustments - by design.

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