The database design fundamentals that keep applications fast and maintainable as they grow — normalization, indexing, data types, relationships, and planning for scale from day one.
The database is the foundation of almost every application, and it's the layer that's hardest to change once you have real data and users on it. Get the design right early and your app stays fast and maintainable for years; get it wrong and you inherit performance problems and data bugs that are expensive to unwind. These are the database design fundamentals we apply on every product we build.
Core design principles
Normalize (but know when to stop) Normalization reduces duplicate data and keeps it consistent — the same fact isn't stored in five places waiting to disagree. Aim for a well-normalized design, then denormalize deliberately only where read performance genuinely demands it, not by accident.
Index with intent Indexes make reads fast but slow writes and use space. Index the columns you actually filter, join, and sort on — and no more. Over-indexing is as real a problem as under-indexing.
Choose the right data types Use appropriate, specific types (proper date/time types, correct numeric precision, sensible string lengths). The right types save storage, speed up queries, and prevent whole classes of bugs.
Model relationships correctly Use foreign keys to enforce relationships and integrity at the database level, so bad data can't creep in through application bugs.
Designing for scale from the start
- Anticipate data growth — a design that's fine at 10,000 rows can crawl at 10 million. Think about volume early.
- Know your read vs. write patterns — a read-heavy app optimizes differently from a write-heavy one.
- Plan backups and recovery before you need them, and test restoring.
- Consider partitioning for very large tables as you grow.
- Document the schema so the next developer (or the next you) understands it.
SQL vs. NoSQL
For most business applications — CRMs, ERPs, dashboards with clear relationships and transactions — a relational database like PostgreSQL is the right default, and it's our go-to. NoSQL fits specific needs (huge scale, flexible schemas, certain document/real-time workloads). Choose based on your data's shape, not hype.
Frequently asked questions
Should I use PostgreSQL or MySQL? Both are excellent. PostgreSQL is our default for its robustness, features, and standards compliance; MySQL is also a solid choice. Either beats picking NoSQL by default for relational business data.
How much should I normalize? Normalize to keep data consistent, then denormalize selectively where measured read performance requires it. Don't denormalize speculatively.
When should I add indexes? On columns used in WHERE, JOIN, and ORDER BY clauses of your common queries. Measure with EXPLAIN rather than guessing — see our SQL performance guide.
Building an app that needs to scale reliably? Talk to us — sound database design is built into everything we ship.