DB-01
Schema Design
Data models designed for the access patterns the application has, not the data model that looked clean on paper.
SchemaModeling
Sheet · Data
Service · Database Architecture
Databases that scale predictably.
Schema design, indexing, migrations, and query optimization. PostgreSQL by default, Firebase or Redis where the access pattern demands it.
Sheet 01 · Overview
The data layer outlasts every framework.
Frameworks come and go. The data model usually outlasts every rewrite, and the database becomes the slowest thing to change. That is why schema design earns more time at the start than almost anywhere else in the build.
We design data models for the access patterns the application actually has, index for the queries that actually run, and write migrations that survive backward-compatible deploys. PostgreSQL by default, with Prisma or hand-written SQL depending on team preference.
For existing databases, we audit indexes, profile slow queries, tune connection pools, and rehearse migrations against staging copies of real data before they touch production.
Sheet 02 · Schedule of Capabilities
Capabilities delivered.
6 core areas
Scoped per brief
Scoped per brief
DB-02
Indexing & Query Tuning
EXPLAIN ANALYZE-driven index strategy, query rewrites, and pagination patterns that do not break at 100k rows.
IndexesEXPLAIN
DB-03
Migrations
Forward-only migrations with backward-compatible deploys. Rehearsed in staging. Documented before they ship.
MigrationsPrisma
DB-04
Connection Pooling
pgBouncer or Prisma's pool. Configured for the actual concurrency the application produces.
pgBouncerPooling
DB-05
Read Replicas & Sharding
When traffic justifies it: read replicas, partitioning, and sharding strategies that avoid hot keys.
ReplicasSharding
DB-06
Backup & Recovery
Managed backups, retention policies, point-in-time recovery, and rehearsed restore procedures.
BackupsPITR
Sheet 03 · Process
How we shape the data.
- Phase 01
Audit
Schema review, query profile, index inventory, and access-pattern documentation. Output: a written database health report.
- Phase 02
Quick wins
Missing indexes added, unbounded queries paginated, hot tables tuned. Performance improvements typically visible within days.
- Phase 03
Structural work
Schema refactors, migration adoption, replica setup, and partitioning when traffic justifies it — each behind feature flags and tested in staging.
- Phase 04
Operate
Retained data engineering: ongoing query review, migration assistance, capacity planning, and backup verification.
Sheet 04 · Stack
Tools we reach for.
PostgreSQLPrismapgBouncerKnexDrizzleFirebase FirestoreFirebase Realtime DBRedisSQLiteDigitalOcean Managed PGAWS RDSSupabaseNeon
Sheet 05 · FAQ
Questions, answered.
PostgreSQL or something else?
PostgreSQL is our default — battle-tested, JSON-capable, and good enough for most workloads we ship. Firebase for realtime needs, Redis for caching and queues, SQLite for embedded contexts. NoSQL only when the access pattern genuinely demands it.
Do you handle schema migrations?
Yes — Prisma Migrate, knex, or hand-written SQL. Forward-only migrations. Backwards-compatible deploys. Production migrations always rehearsed against a staging copy of real data.
What about performance tuning?
Index analysis, query profiling (EXPLAIN ANALYZE), connection pool sizing, vacuum and autovacuum tuning, and read-replica strategy. Most "slow database" tickets are missing indexes or unbounded queries.
Can you fix a database that is already in production?
Most engagements start with an existing database. Index audits, query rewrites, schema cleanup, and connection pool tuning are the typical first interventions.
Do you support managed databases?
Yes — DigitalOcean Managed PostgreSQL, AWS RDS, Supabase, Neon, and PlanetScale. We avoid running your own database unless there is a specific reason to.
Sheet 07 · Start a brief
Data layer worth building on.
Send us your slowest query and your scaling target. We'll come back with an index strategy and a migration plan.