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Database Design & Normalization

Relational schema design. Normalization vs. denormalization trade-offs.

Author: Paweł Wiszniewski · Updated:

Key takeaways

Relational schema design. Normalization vs. denormalization trade-offs.

  • ›Normalized schema for data integrity and consistency.
  • ›Denormalization recommendations—computed columns for high-frequency queries.
  • ›Proper data type selection—VARCHAR vs TEXT vs JSONB implications.

Price: from €1,500

SERVICE DETAILS

I design database schemas with proper normalization (3NF+) for relational databases. I analyze access patterns, define key queries, balance consistency vs. performance, and implement proper constraints, foreign keys, and indexes.

› INVESTMENT:

from €1,500
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Who this is for

  • Startups beginning a new product that want to avoid an expensive database rebuild a year later.
  • Apps where data gets duplicated and reports show different numbers.
  • Teams planning a move from NoSQL to PostgreSQL or the other way round.
  • Companies with a growing number of users and queries.

What you get

  • Access pattern analysis: what the app reads and writes most often.
  • A relational schema with normalization and deliberate denormalization.
  • Constraints, foreign keys and indexes that protect consistency and performance.
  • A zero-downtime schema migration strategy.
  • An ERD diagram and documentation of design decisions.

How results are measured

  • Execution time of the most frequent queries.
  • Inconsistencies and duplicate records in the data.
  • Database size and growth rate.
  • Time needed to ship a feature that changes the schema.

Key Benefits

Normalized schema for data integrity and consistency.

Denormalization recommendations—computed columns for high-frequency queries.

Proper data type selection—VARCHAR vs TEXT vs JSONB implications.

Constraints & validations—database-level integrity checks.

Migration strategy—safe schema changes with zero downtime.

The Process

1

Requirements Analysis

Understand entities, relationships, and query patterns.

2

Schema Design

Create ER diagram, define tables, relationships, and constraints.

3

Optimization

Add strategic denormalization and computed columns for performance.

4

Implementation

Create migration scripts, test on test data, deploy.

Frequently Asked Questions

When should I denormalize?

When the read:write ratio is high (95%+ reads). Examples: cached counts, cached aggregations.

What data type for large text?

TEXT for ≤1GB, JSONB for structured nested data, external storage (S3) for >1GB.

How to migrate schema in production?

Expand (add column), Migrate (fill data), Contract (remove old column). Zero-downtime deployment.

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com