How a package manager backend stores its data: packages and immutable published versions, integrity hashes, dependency ranges, movable dist tags like latest and beta, owners and scoped access tokens, download rollups, deprecations and yanks.
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.