Database Development Services

Why Database Development

Every application relies on its database. A good design keeps data consistent, queries fast and future changes manageable; a poor one slows the whole product down and makes every feature harder. As data grows, issues that were invisible at launch — missing indexes, inefficient queries, locking — become outages.

We design new databases, optimise existing ones and handle migrations across PostgreSQL, MySQL, SQL Server, MongoDB, Redis and Elasticsearch/OpenSearch.

When Database Development is the right choice

What we build with Database Development

Our Database Development toolkit

Common performance fixes

• Adding or correcting indexes for the most frequent queries
• Removing N+1 query patterns in application code
• Paginating large result sets efficiently
• Moving reporting queries to read replicas
• Caching hot data in Redis
• Archiving old data to keep tables lean

A focused database review often delivers the largest performance improvement for the least effort.

Choosing the right database

For most new products, PostgreSQL is our default: it is reliable, open source, supports JSON, full-text search and vector extensions, and is offered as a managed service by every major cloud.

Signs your database needs attention

• Pages or reports slow down as data grows
• Frequent deadlocks or timeouts at busy times
• No one is confident backups can actually be restored
• Schema changes are risky and done manually
• Duplicate or inconsistent data across tables or systems
• The database version is near or past end of support

Our approach to performance tuning

We start with evidence, not guesses: slow-query logs, execution plans and wait statistics show where time is actually spent. Fixes usually come from the right indexes, rewriting a handful of expensive queries, removing N+1 query patterns in the application, adding caching or archiving old data. Hardware upgrades come last. Every change is measured before and after, and schema changes are delivered through versioned migrations so they are repeatable across environments.

Migrations between database platforms

Moving between databases — for example from SQL Server or Oracle to PostgreSQL to reduce licensing costs, or from self-managed servers to a managed cloud service — is a common and achievable project when done carefully. The work involves converting schemas and data types, rewriting stored procedures and vendor-specific SQL, migrating data with tools such as AWS DMS or pgloader, and running both systems in parallel to compare results. We plan cut-over windows, rollback paths and validation checks on row counts and checksums so the business can switch with confidence.

Data modelling done right

Most database problems start with the data model. Tables designed around one screen, duplicated data that drifts out of sync and missing constraints all create bugs that no amount of query tuning fixes. We design models that reflect the business rather than the current user interface:

• Normalised core tables with foreign keys, unique constraints and check constraints enforcing rules in the database itself
• Deliberate denormalisation only where reads clearly benefit, with a plan to keep copies consistent
• Clear naming conventions and documentation of what each table and column means
• Soft deletes, audit columns and history tables where the business needs to know what changed and when
• Multi-tenant designs that make it hard to leak one customer’s data to another

Indexing strategy

Index names and types vary by database; this table uses PostgreSQL terms.

We base indexing on real query plans and production query statistics rather than guesswork, and remove unused indexes that only slow down writes.

Backups, recovery and high availability

A backup that has never been restored is a hope, not a backup. We design recovery around two numbers you agree with the business: how much data you can afford to lose (recovery point objective) and how long you can be offline (recovery time objective).

• Automated backups with point-in-time recovery where supported
• Copies stored in a separate account or region
• Scheduled restore tests with documented steps and timings
• Replicas or managed high-availability options sized to your recovery targets
• Monitoring and alerts for replication lag, storage growth and failed backups

Database security

• Separate database users for applications, migrations, reporting and administrators, each with least privilege
• Encryption in transit and at rest, with keys managed by your cloud provider’s key service
• Parameterised queries everywhere to prevent SQL injection
• Sensitive columns masked in non-production copies
• Audit logging for administrative access and schema changes

Schema changes without downtime

Changing a busy database safely needs discipline. We use version-controlled migrations run by the deployment pipeline and follow the expand-and-contract pattern: add new columns or tables first, deploy code that writes to both, backfill in small batches, switch reads, and remove the old structure only after everything has moved. Large tables are altered with online schema-change tools where needed, so users never see a maintenance page.

Database work often sits alongside data engineering and cloud migration projects.

Reporting without slowing production

Heavy reports and analytics queries can compete with customer traffic for the same database. Common remedies include read replicas dedicated to reporting, materialised views refreshed on a schedule, and moving analytical workloads to a separate warehouse fed by change-data-capture. The right choice depends on how fresh reports need to be and how complex the queries are.

Questions we ask before database work

• Which queries matter most to users, and how fast must they be?
• How quickly is data growing, and which tables grow fastest?
• What are your recovery point and recovery time objectives?
• Who needs access to production data, and why?
• Are there regulatory rules on where data is stored or how long it is kept?

We document the answers in a short database charter so future changes are judged against the same goals.

That charter also guides decisions about new tables, indexes and integrations as the product grows.

Work with us

Part of our data & AI technologies. See also data engineering and performance testing.

This page is part of Data & AI Technologies — see the full overview for related services, approach and pricing.