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.
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.
An internal service desk modelled properly: a service catalogue, incidents and requests, ordered approval chains, change requests with risk and rollback plans, linked assets, versioned knowledge base articles, SLA clocks that pause, and satisfaction ratings.