Backend & Cloud Engineering

Database Architecture & Design

Get the data model right, because everything else depends on it.

Typical timeline: 1-3 weeks for design review, longer for refactoring work
Category: Backend & Cloud Engineering
Engagement: Fixed-scope phases
You receive: Code, data & documentation

The data model is the most expensive thing to change later and the most consequential thing to get right early. We design schemas that enforce correctness in the database itself, perform well as volumes grow, and remain comprehensible to the next developer.

The problem

A data model that fights the business rather than describing it

Tables accumulate columns like status_2 and remarks_new. Relationships are implied rather than enforced, so contradictory records exist. Queries slow down as data grows and are fixed by adding more indexes until writes become slow too.

You will recognise this if

Columns with names like extra_field, remarks_2 or temp_status
The same piece of information is stored in several places and they disagree
Reports take minutes to run and get slower every month
Nobody can safely change the schema because nothing is documented

What you get out of it

The difference this makes

Invalid data becomes impossible

Constraints, foreign keys and check rules enforced by the database mean application bugs cannot create contradictory records.

Queries that stay fast

Indexing driven by actual query patterns, with execution plans reviewed, so performance holds as volume grows.

A model people understand

Clear naming and documented relationships mean a new developer can read the schema and understand the business.

Room to grow

Partitioning, archival and read-replica strategies planned before they are urgent, rather than during an outage.

What we build

Conceptual and logical data modelling from your business processes
Normalised schema design with deliberate, documented denormalisation
Constraint design: foreign keys, uniqueness, checks and exclusion constraints
Indexing strategy based on real query patterns, including composite and partial indexes
Query analysis and optimisation using execution plans
Partitioning and archival strategy for large tables
Audit and history tables for change tracking
Row-level security policies for multi-tenant data isolation
Backup, point-in-time recovery and tested restore procedures
Entity-relationship documentation and a data dictionary

How it gets used

A new application being designed properly

Schema design before development starts, which is the cheapest point at which to get it right.

An application whose database has become the bottleneck

Query analysis, index correction and schema refactoring to recover performance without rewriting the application.

A multi-tenant SaaS product

Tenant isolation enforced at the database level, the most important and least retrofittable decision in a SaaS architecture.

Technology we typically use

You do not need to care about this list — but your next developer will.

PostgreSQL
MongoDB
Redis
MySQL / MariaDB
pgvector
Schema migration tooling

Timeline: Typically 1-3 weeks for design review, longer for refactoring work

Questions

Database Architecture & Design — common questions

Something else on your mind? Ask us directly — it is usually faster than reading.

PostgreSQL or MongoDB?

PostgreSQL for almost all business applications: your data is relational, you need transactions and constraints, and PostgreSQL handles JSON well when you do need flexible fields. MongoDB suits genuinely document-shaped data with varying structure. Choosing MongoDB to avoid designing a schema usually results in schema problems appearing later in application code instead.

Can you fix our existing database without downtime?

Usually yes, in stages: add new structures alongside the old, migrate data in batches, switch reads over, then remove the old structures. Each step is reversible. Some changes do need a short maintenance window, which we plan and communicate in advance.

How much data can PostgreSQL handle?

Far more than most businesses will generate — tables with hundreds of millions of rows perform well with correct indexing and partitioning. Performance problems at normal business scale are nearly always design problems rather than capacity limits.

Businesses that need this often need one of these as well.

PostgreSQL & MongoDB DevelopmentDatabase work done properly: queries, indexes, backups and recovery.Performance OptimizationFind the actual bottleneck, fix it, and prove the improvement.Python & FastAPI DevelopmentFast, well-tested backend services that hold up under real load.Data Migration ServicesMove years of data to a new system without losing or mangling it.

Thinking about database architecture & design?

Tell us about your situation. We will tell you whether this is the right service, what it would involve, and roughly what it would cost.

Get a free consultation