An experimentation platform end to end: experiments and weighted variants, deterministic hash bucketing, exposure events, metric definitions, per-variant aggregates with sufficient statistics, significance results and staged rollout.
How a product analytics service stores its data: projects and a discovered event registry, raw events with JSONB properties, anonymous and identified users with identity merges, sessions, funnels, cohorts, retention reports and precomputed metric snapshots.
How a campaign sender stores its data: lists and contacts with custom fields, rule-based segments, campaigns with A/B variants, one send row per recipient, opens and clicks, bounces and suppressions, and drip automations.
A support desk modelled properly: tickets in queues, one thread that mixes public replies with internal notes, SLA clocks measured in business hours with breach tracking, macros, tags, merges and CSAT.
Shared-database multi-tenancy done properly: organizations as tenants, global users joined through memberships, invitations, scoped API keys, plans, subscriptions, metered usage and an append-only audit log.