How an API platform is modelled: consumers and applications, hashed API keys with scopes and rotation, upstream services and routes, rate limit policies, durable usage counters, rolled-up aggregates, sampled request logs, plans, quotas and overage billing.
How a build system models its data: versioned pipeline definitions, immutable runs triggered by a commit, stages with approval gates, a job DAG stored as edges, runners and capabilities, attempts and retries, artifacts, caches and streamed logs.
How an error monitoring service models its data: projects and releases, raw events with stack frames and breadcrumbs, grouping into issues by fingerprint, resolve and regression detection, alert rules and event sampling.
How a feature management platform stores its data: flags and variations, per-environment settings, ordered targeting rules, reusable segments, percentage rollouts with deterministic bucketing, scheduled changes and a full audit trail.
How a code hosting platform models its data: users and organisations, repositories, refs and commits, issues and pull requests sharing one number sequence, reviews with comments anchored to a file and line, labels, milestones and workflow runs.