Development Guide

Database Selection Guide for Web Projects - Web Development Guide for Business

Everything you need to know about database selection guide for web projects. A practical guide for Australian business owners from Harvard-educated developers.

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Your database choice shapes everything downstream - query performance, reporting flexibility, team hiring, hosting cost, and how painful the first migration will be when the product changes. There is no universally "best" database; there are trade-offs that land differently depending on the shape of your data, the read/write patterns, and who will maintain it.

This guide walks through the decision criteria we use on client projects, then summarises the leading options for Australian mid-market web applications so you can make a defensible choice before a line of code is written.

How to Decide

The factors that drive a sound selection.

Data Shape and Relationships

Highly relational data (customers, orders, invoices, line items) belongs in a relational database where joins and referential integrity are first-class. Document-shaped data (flexible schemas, nested attributes, rapid iteration) suits a document store.

Read vs Write Patterns

Reporting-heavy workloads reward SQL engines with rich aggregation and indexing. Write-heavy, append-style workloads (event logs, telemetry, sensor data) favour time-series or columnar stores.

Consistency Requirements

If stale reads would cause financial or legal problems (billing, inventory, bookings), pick a database with strong ACID guarantees. Eventually consistent stores trade correctness for scale and should only be used where that trade-off is explicit.

Query Complexity

Multi-table joins, window functions, and ad-hoc reporting queries are where mature SQL engines shine. If most queries fetch a single document by key, a simpler key-value or document store may be enough.

Team and Hiring

PostgreSQL and MySQL skills are widely available in the Australian market. Picking an exotic database commits your next hire to a small talent pool - weigh the engineering benefit against the hiring cost.

Operational Surface Area

Managed offerings (RDS, Aurora, Supabase, MongoDB Atlas) hide most operational pain. Self-hosted databases give you control but require backups, patching, replication, and monitoring that your team must actually do.

Your Options

A practical review of the leading choices.

PostgreSQL

A mature, open-source relational database with excellent JSON support, rich indexing, and strong ACID guarantees. The pragmatic default for most Australian web projects.

Best for

Transactional applications with related data (SaaS, e-commerce, bookings, finance)
Teams that expect to run complex reporting queries against live data
Projects that occasionally need document-style flexibility (JSONB columns)
Anyone who wants the broadest Australian hiring pool

Watch out for

Write throughput ceiling on a single node - shard or partition before scale becomes painful
Default configuration is conservative; tune shared_buffers and work_mem for real workloads

MySQL / MariaDB

A battle-tested relational database behind a huge share of the web. Fast for simple workloads, well-understood operations, and deep hosting support.

Best for

Content-heavy sites and traditional CRUD web apps
Projects with existing MySQL expertise on the team
Shops standardising on LAMP or WordPress ecosystems

Watch out for

Weaker JSON and analytics story than PostgreSQL
Historical gotchas around strict mode, character sets, and default collation - audit these before production

MongoDB

A document database that stores flexible, nested JSON-like records. Good fit when the schema genuinely varies per record and the access pattern is document-centric.

Best for

Product catalogues with wildly different attribute sets per item
Content management and CMS-like workloads
Projects where documents are read and written as a whole unit

Watch out for

Multi-document transactions exist but are slower - not a drop-in replacement for relational integrity
Engineers often recreate joins in application code, losing the flexibility MongoDB is meant to give you

SQLite

A file-based SQL engine embedded in the application process. Zero operational overhead and surprisingly capable for small to mid-size workloads.

Best for

Prototypes and early-stage products before traffic justifies a server
Single-writer workloads such as internal tools and background job stores
Edge or offline-first applications

Watch out for

One writer at a time - concurrent writes serialise
Not suited to horizontally scaled web tiers unless paired with careful replication (e.g. Litestream)

Redis

An in-memory key-value store - usually a cache or session store, not a primary database. Sub-millisecond reads and writes at the cost of durability guarantees.

Best for

Caching expensive database queries and rendered fragments
Rate limiting, session storage, and pub/sub signalling
Queues and job coordination (Bull, Sidekiq, Celery)

Watch out for

Data lives in memory - capacity planning is about RAM, not disk
Treating it as a source of truth usually ends in tears; pair it with a durable store

DynamoDB / Firestore

Managed, serverless key-value and document stores that scale horizontally with predictable latency. Billed per request, so cost tracks usage rather than provisioned capacity.

Best for

High-scale, key-lookup workloads with simple access patterns
Event-driven and serverless architectures on AWS or Google Cloud
Mobile and web apps that need real-time sync out of the box (Firestore)

Watch out for

Query flexibility is limited - you design access patterns up front, not ad-hoc later
Vendor lock-in is real; migration away is a project, not a switch

Our Recommendation

For most Australian mid-market web projects, start with PostgreSQL. It handles relational data well, speaks JSON when you need flexibility, runs cheaply on managed services, and has the deepest local hiring pool. Add Redis once you have a real caching or queueing need - not before.

Reach for a document database only when your schema is genuinely irregular and document-shaped, not because relational modelling feels like extra work. Reach for DynamoDB or Firestore only when your access pattern is narrow and predictable, your scale is already large, and you accept the vendor coupling. If you are not sure, the default answer is PostgreSQL and a thoughtful schema.

Frequently Asked Questions

Most projects deliver a working prototype in 2 weeks and complete within 4-8 weeks. Complex applications may take longer. We deliver in phases so you see value quickly.

Every project is scoped individually based on complexity, integrations, and feature requirements. Most projects fall within our standard tiers. Book a free assessment to get a transparent, fixed-price quote.

Absolutely. You get 3 months of hands-on support after launch. For ongoing development, we offer flexible retainer packages tailored to your needs.

Yes. We integrate with 150+ business tools and can build custom connectors for specialist software. Your new application will work seamlessly with your existing tech stack.

We primarily build with Next.js, React, TypeScript, and Node.js. We also work with Python, PostgreSQL, and various cloud platforms. We choose the best technology for your specific requirements.