Database Design Principles
Good database design is the foundation of any scalable application. Poor decisions at the schema level compound over time, leading to slow queries, fragile data integrity, and expensive refactors. A well-designed database minimizes redundancy, enforces correctness, and scales predictably as data volume and access patterns grow.
This article expands on core principles every backend engineer should understand, with practical trade-offs rather than dogma.
Normalization
Normalization is the process of structuring data to reduce redundancy and prevent update anomalies. In practice, this means decomposing tables so that each fact is stored once and only once.
Why normalization matters
Without normalization:
• Updates require changing the same data in multiple places
• Inconsistencies inevitably appear
• Deletions can unintentionally remove important information
Normalization directly improves data integrity, not performance.
Common normal forms (briefly)
• 1NF: Atomic values, no repeating groups
• 2NF: No partial dependency on a composite key
• 3NF: No transitive dependency (non-key → non-key)
Most production systems target 3NF as a baseline.
Reality check: normalization is not free
Highly normalized schemas:
• Increase the number of joins
• Can degrade read performance for analytical or read-heavy workloads
This is why mature systems often start normalized and selectively denormalize later, once access patterns are proven by real traffic—not assumptions.
Indexing
Indexes are the primary performance lever in relational databases, but they are also one of the most misunderstood.
What an index actually does
An index is a data structure (typically B-tree or hash-based) that allows the database to locate rows without scanning the entire table.
Used correctly, indexes can reduce query time from O(n) to O(log n).
When to add an index
Index columns that are:
• Frequently used in WHERE, JOIN, ORDER BY, or GROUP BY
• Highly selective (many distinct values)
Avoid indexing:
