Service
Database Design & Optimization
Database design & optimization services for MySQL, PostgreSQL & MongoDB. Faster queries, reliable data, expert administration. Get a free database audit today.
Why work with us on database design & optimization
Our database experts provide comprehensive database solutions including design, development, optimization, and administration. With expertise in both SQL and NoSQL databases, we ensure your data is structured efficiently, performs optimally, and scales with your business needs.
Database design and architecture planning
Performance optimization and query tuning
Data migration and database modernization
Backup and disaster recovery solutions
Database security and compliance implementation
Replication and high availability setup
Data warehousing and analytics solutions
Database monitoring and maintenance
Get started
Technical proposal call
Scope and timeline agreed
Development and delivery
Stack
Technologies we use
MySQL
expert
PostgreSQL
expert
MongoDB
expert
Redis
advanced
SQL Server
advanced
Oracle
advanced
Elasticsearch
advanced
Cassandra
intermediate
Amazon RDS
advanced
Google Cloud SQL
advanced
Docker
advanced
Kubernetes
intermediate
Grafana
advanced
Prometheus
advanced
Apache Spark
intermediate
ETL Tools
advanced
Choosing
Which database fits your data?
This is the first decision and the hardest to reverse. Most products have one clear answer once you describe how the data is read and written.
Relational
Structured data, transactions, reporting.
PostgreSQL first, SQL Server in Microsoft estates, MySQL for existing Laravel systems. The default for almost every application, and with JSON, full-text search and pgvector in PostgreSQL, it covers far more than it used to.
PostgreSQL · SQL Server · MySQL · Supabase
Vector and search
AI retrieval over your own content.
pgvector or Supabase Vector to store embeddings beside the rows they describe, so RAG features answer from your data without a separate service. We run this over 100,000+ documents in production.
pgvector · Supabase Vector · full-text search
Specialised stores
Cache, documents, graphs, streams.
Redis for cache, sessions and queues. MongoDB for genuinely document-shaped data. Neo4j when relationships are the point. Kafka or SQS for event streams. Each added only where a relational model would struggle.
Redis · MongoDB · Neo4j · SQS · Kafka
Engineering
What we do
Schema design
Normalised where it protects integrity, denormalised where reads demand it, with tenant isolation and audit columns designed in from the start. Built against realistic data volumes, not a ten-row fixture.
Query and index optimisation
Profiling from the real slow-query log and execution plans. Index design, query rewrites, ORM fixes for N+1 patterns, and summary tables for reporting. Typically an order of magnitude before any hardware change.
Import and sync pipelines
Batched, queued, resumable imports with pagination and rate-limit handling against source APIs, upserts, and visible progress. Built for 30,000+ records per run.
Vector search for AI
Embedding pipelines into pgvector or Supabase Vector, hybrid search combining vectors with full-text, and automatic re-indexing when content changes, so RAG answers stay current.
Migration and modernisation
Database-to-database and on-premise-to-cloud migrations, schema redesigns and post-acquisition consolidations, done incrementally with validation and a tested rollback at every step.
Reliability
Automated backups with tested restores, replication for read scaling and failover, monitoring on the queries that matter, and documented recovery objectives the business can hold us to.
Applications
Data problems we have solved
Records in the millions
The REI Op-Center platform imports tens of thousands of rows per upload and lets customers sort, filter and manipulate records numbering in the millions, on a schema rebuilt for that scale.
Marketplace order pipeline
Datamag: a resilient import system processing 30,000+ order records per sync, handling pagination and API rate limits, with Lambda and SQS workers so jobs survive logout.
Enterprise SQL Server
Designed and optimised SQL Server databases handling millions of records on production enterprise systems, with role-based access control isolating each tenant’s data.
Retrieval over 100,000 documents
A Supabase Vector index over 100,000+ posts feeding a RAG chatbot, with an n8n workflow that indexes new content automatically so the knowledge base never goes stale.
Analytics backends
E-commerce analytics aggregating order and customer data into reportable metrics on PostgreSQL, structured so dashboards stay fast as history accumulates.
Document workflow at scale
The LawHQ eSignature platform: documents, signatures, templates, contacts and a hash-chained activity log, all queryable for compliance review.
Working together
Three ways to start
Most database work begins with a review. We will tell you what is actually slow and what it would take to fix, before anything is rebuilt.
Performance review
A fixed-scope audit of schema, indexes and the slowest queries, returning a prioritised fix list with the expected gain for each.
Book a review →Design for a new product
Schema, isolation model and pipeline design for a product that has to scale, delivered with the application or as a standalone engagement.
Get a scoped quote →Migration
Move between databases or to the cloud incrementally, with validation at each step and a tested rollback.
Scope a migration →FAQ
Database questions we get asked
PostgreSQL, MySQL, SQL Server or MongoDB?
PostgreSQL for almost everything new: it is the most capable open-source database, it handles JSON, full-text search and vectors natively, and Supabase makes it easy to run. SQL Server when you are in a Microsoft estate and want its tooling and support. MySQL when you are extending an existing MySQL or Laravel system. MongoDB only when your data is genuinely document-shaped and schema-free, which is rarer than the marketing suggests.
Our queries got slow as data grew. What usually causes that?
Missing or wrong indexes, queries written against ten test rows that now scan ten million, N+1 patterns from the ORM, and reports that aggregate on the fly instead of from a summary table. We profile with the actual slow query log and execution plans, fix the top offenders, and usually get an order of magnitude back before touching hardware.
Can you handle bulk imports and syncs without locking the application?
Yes. Imports run as queued background jobs in batches, with pagination and rate-limit handling against the source API, upserts instead of delete-and-reload, and progress the user can see. The Datamag pipeline processes more than 30,000 marketplace orders per sync this way and keeps working after the user logs out.
What is pgvector and when do we need it?
A PostgreSQL extension that stores embeddings and runs similarity search, so retrieval for AI features happens in the database you already run instead of a separate vector service. We use it and Supabase Vector for RAG pipelines, including one indexing more than 100,000 posts. You need it the moment an AI feature has to answer from your own data.
How do you approach a schema for a multi-tenant SaaS?
Usually a shared schema with a tenant key on every table and row-level security enforcing it, which scales well and keeps operations simple. Schema-per-tenant or database-per-tenant when isolation requirements or data residency demand it. The decision is made early because it is expensive to change, and we document the reasoning.
Do you do data migration between databases or systems?
Yes: MySQL to PostgreSQL, on-premise SQL Server to cloud, legacy schemas to redesigned ones, and consolidations after acquisitions. We migrate incrementally with validation at each step, run both systems in parallel where the risk warrants it, and never cut over without a tested rollback.
When should we add Redis?
When the same expensive result is read far more often than it changes, for session storage, rate limiting, queues, or leaderboards and counters that must be fast. It is a complement to the primary database, not a replacement, and adding it too early creates cache-invalidation problems you did not need.
How do you handle backups and disaster recovery?
Automated point-in-time backups with tested restores, because an untested backup is a hope, not a plan. Replication for read scaling and failover where uptime requires it. We document the recovery time and recovery point you actually get, so the business can decide whether that is enough.
Can you review our existing database without rebuilding it?
That is the most common engagement. We audit schema, indexes, the slowest queries, growth trends and the ORM usage, and return a prioritised list of fixes with the expected gain for each. Most of the improvement comes from the first few items, and none of it requires a rewrite.
Do you work with graph or time-series databases?
Neo4j when relationships are the data — recommendations, fraud rings, org charts, dependency graphs — and the queries are about traversal. Time-series stores for high-volume metrics and telemetry. Both are used only where a relational model would genuinely struggle; most products do not need them.
How we work
Our development process
Assessment
Analyzing current database structure and performance requirements
1-2 days
Design & Planning
Creating optimal database schema and implementation strategy
2-3 days
Implementation
Database setup, migration, and optimization implementation
1-4 weeks
Testing & Monitoring
Performance testing, monitoring setup, and documentation
3-5 days
Why us
Benefits of working with us
Certified database administrators and developers
Proven track record with enterprise-scale databases
Performance optimization expertise
24/7 monitoring and support options
Data security and compliance best practices
Cost-effective database solutions
Ready to hire our database design & optimization experts?
A senior engineer replies within one business day with a real technical read on your project.
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