Database Architecture & Data Systems
Persistence, schema design, caching, time-series storage, and data infrastructure choices matched to the product workload.
What this covers
Postgres, SQLite, Redis, time-series stores, document databases, and the logic for choosing between them
This service is about selecting the right storage model, schema structure, caching layer, and runtime persistence approach for the product instead of defaulting to one database for every job.
Schema
Data model
Caching
Performance
Time-Series
Specialized loads
Typical scope
Operational product databases, caches, analytics stores, and specialized persistence
That includes application schema design, account and product data modeling, caching strategy, time-series data, realtime persistence, and database choices that need to support both the current product and likely growth paths.
Why it matters
The storage model shapes the product more than many teams realize
A poor database choice creates friction everywhere else. A good one makes the product easier to scale, easier to reason about, and far less fragile when new features or heavier loads arrive.
Database range
Relational, document, cache, and time-series systems selected for the workload
The range here includes PostgreSQL, SQLite, Redis, KeyDB, TimescaleDB, QuestDB, DragonflyDB, SpacetimeDB, MongoDB, and adjacent tools. The real skill is knowing when each model helps and when it becomes the wrong constraint.
Relevant proof
- Skills page coverage for the database and platform range
- Hosting service coverage for the surrounding infrastructure choices
- Portfolio coverage for APIs, data systems, and runtime support work
- NativeCharts as the deeper proof of time-series and systems-oriented thinking
Related Paths
Need the right data model and storage layer from the start?
If the product depends on the right schema, cache, or specialized data store, we can choose that architecture deliberately instead of patching around the wrong decision later.