Data Layer

Data Layer & Modeling

1. Start with the real need

Before reaching for a cloud database, clarify:
  • Is persistence needed beyond this process run? If not, in‑memory is enough for a demo.
  • Is a single device/user sufficient? If yes, local storage can be fine.
  • Is multi‑user or sharing required? If yes, a managed backend is appropriate.
  • Does the toolchain already include a database integration? Use it. Example: Lovable includes Supabase integration out of the box.
  • Will this be deployed soon? Heavy self‑managed databases slow down first shipping.
Rule of thumb: begin with the lightest option that meets the need; move up the ladder only when a real constraint appears.

2. The storage ladder (from lightest to heaviest)

2.1 In‑memory (ephemeral)

What it is: Store data in RAM inside the running process (maps, lists, simple caches).
Use when: throwaway demos, quick experiments, one‑off scripts that export results and exit.
Caveats: lost on restart; not shared across devices/processes; unsuitable for long sessions.

2.2 Local on‑device storage

Web: localStorage, sessionStorage, IndexedDB for larger structured data. Desktop/CLI: SQLite (or libSQL) file next to the app; DuckDB for local analytics. Mobile: platform stores (e.g., SQLite/Room/Realm, Keychain/Secure Storage for small secrets).
Use when: single‑user tools, offline‑first utilities, fast iteration without network dependencies.
Caveats: device‑bound; syncing is extra work; avoid placing secrets in browser storage; plan backups explicitly.

2.3 Files on disk (JSON/CSV/Parquet + a folder)

What it is: Write structured outputs to files; organize with a simple folder convention.
Use when: data collection runs, quick import/export between tools, early prototypes of content pipelines.
Caveats: no safe concurrent writes without care; manual indexing/search; can become messy without naming rules.
Examples: Supabase (Postgres + Auth + Storage), Firebase/Firestore, Neon/RDS/Cloud SQL (Postgres), Planetscale (MySQL).
Why start here once multiple users or devices are involved:
  • Auth and basic access control available immediately.
  • Object storage for files (PDFs, images, audio) with signed URLs.
  • Mature SQL/NoSQL choices with SDKs, migrations, and backups.
Note: With Lovable, Supabase works out of the box, so team prototypes can ship quickly.
Caveats: some setup and schema design still required; far lighter than self‑hosting a database.

2.5 Specialized services (add only when necessary)

Search engines: OpenSearch/Elasticsearch for text search and aggregations. Caches/queues: Redis for fast lookups, job queues, rate‑limits. Data warehouses: BigQuery/Snowflake for heavy analytics (not primary app storage). File/object storage at scale: S3/GCS/Supabase Storage for large media libraries.
Caveats: extra moving parts, credentials, and deployment overhead; adopt when simpler options no longer suffice.

3. Common app scenarios and suitable options

3.1 Idea demo / hypothesis check (single device)

  • Goal: validate behavior quickly on one machine.
  • Store: in‑memory or local files; optional SQLite.
  • Notes: short sessions; export results at the end; no auth.

3.2 Solo tool (automation, CLI, desktop)

  • Goal: repeatable local workflow (rename files, summarize PDFs, transform data).
  • Store: SQLite/DuckDB + a files folder for artifacts.
  • Notes: keep schemas small; avoid over‑indexing.

3.3 Team prototype (shared access)

  • Goal: several people use the feature and share results.
  • Store: Supabase (auth + Postgres + storage) or similar managed backend.
  • Notes: basic tables for users/projects/items; per‑user/tenant access rules.

3.4 Production web/mobile app (multi‑tenant)

  • Goal: stable app with roles, auditability, predictable access.
  • Store: managed Postgres/MySQL with auth and storage; consider read replicas and backups.
  • Notes: explicit schemas, migrations, retention/deletion policies.

3.5 Content/file‑heavy app

  • Goal: images, PDFs, audio with previews and sharing.
  • Store: object storage (S3/GCS/Supabase Storage) + database for metadata/permissions.
  • Notes: generate signed URLs; store hashes for deduplication.

3.6 Event logging & analytics

  • Goal: understand behavior and health.
  • Store: append‑only logs (files or a lightweight table) → batch to a warehouse later.
  • Notes: start with minimal fields; add dashboards when signal proves useful.

4. Migration path (move up only when constraints appear)

  1. In‑memory → Local when persistence beyond a single run is required.
  2. Local → Managed DB when sharing, auth, or multi‑device access is needed.
  3. Managed DB → Specialized service when scale or query patterns exceed what a single database comfortably provides.
Carry data in portable formats (CSV/JSON/Parquet) to ease moves. Keep entity names stable even when storage changes.

5. Practical guardrails

  • Keep it small first. One table per entity that matters; avoid generic blobs.
  • Avoid secrets in client storage. Prefer platform secret stores or server‑side env vars.
  • Model unknowns explicitly. Use NULL/controlled enums rather than invented values.
  • Name files and folders predictably. Dates, IDs, and clear prefixes prevent chaos.
  • Plan retention and deletion. Even prototypes benefit from a simple cleanup rule.
  • Prepare basic backups. A periodic dump or snapshot reduces recovery pain.

6. Summary

Data must live somewhere, yet a heavy database is rarely the right first step. Begin with the lightest workable option (in‑memory or local), then adopt a managed backend once multiple users, auth, or sharing enter the picture — especially convenient when the chosen tool already integrates a provider like Supabase (as with Lovable). Add specialized services only when clear constraints demand them. This ladder keeps demos smooth and deployments straightforward while leaving room to grow.