Database & API
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Data Architecture That Performs at Scale
A poorly designed database is a debt that compounds. Queries that are fast at 10,000 rows become slow at 10 million. CimpleO designs and builds data layers — relational, document, and time-series — that are structured for your access patterns from the start, and optimised before they become a problem.
Database Design & Architecture
We design schemas for your specific access patterns — not generic textbook structures. Relational databases (PostgreSQL, MySQL, MariaDB) for transactional workloads with complex relationships. Document stores (MongoDB) for flexible, high-write workloads. Redis for caching and session management. PerconaDB and other high-availability configurations for production-critical systems.
Performance Optimisation
Slow queries, missing indexes, N+1 problems, and lock contention are addressable — if you know where to look. We audit existing databases, identify bottlenecks with query profiling, implement appropriate indexing strategies, and restructure problematic queries. Performance gains of 10–100× are common in legacy systems that have never been tuned.
API Development
We build RESTful and GraphQL APIs that are clean, versioned, and documented. Endpoints designed around your real use cases, not a generic CRUD layer. Authentication with JWT and OAuth2, rate limiting, and request validation. API contracts that your frontend and mobile teams can depend on not to change without notice.
Third-Party API Integration
Payments (Stripe, PayPal), delivery services (UPS, FedEx, DHL), CRM platforms (Salesforce, HubSpot), mapping (Google Maps, HERE), communications (Twilio, SendGrid) — we integrate the third-party services your product depends on, handling authentication, webhook processing, error recovery, and idempotent retry logic properly.
Our Database Technology Stack
- Relational: PostgreSQL, MySQL, MariaDB, PerconaDB
- Document: MongoDB, Firebase Firestore
- Cache & Queue: Redis, RabbitMQ, Apache Kafka
- Search: Elasticsearch, Algolia
- Time-series: InfluxDB, TimescaleDB
Tell us about your data requirements — we’ll design an architecture that scales with your product.
Frequently Asked Questions
Can you optimise our existing database without a full rebuild?
Yes — and this is often the highest-value engagement we do. Slow query identification with EXPLAIN ANALYZE, index restructuring, N+1 elimination at the ORM layer, connection pooling, and Redis caching layer implementation. Most legacy systems see 10–50× query improvement without schema redesign.
PostgreSQL or MongoDB — which is right for our use case?
PostgreSQL for transactional workloads with complex relationships, financial data, or anything requiring ACID guarantees. MongoDB for high-write, schema-flexible workloads where JSON document structure maps naturally to your domain. We'll give you a direct recommendation based on your access patterns, not a 'it depends' non-answer.
How do you handle database migrations in production without downtime?
Expand-contract pattern: add the new structure while keeping the old, backfill data, switch traffic, then remove the old structure. For large tables, we use batched background migrations with row-level locking to avoid table locks. We plan and test migrations in staging with production-scale data snapshots before touching production.
Do you build the API as well as the database?
Yes — database design and API development are typically done together. Schema design that ignores API access patterns creates impedance mismatches that cause performance issues later. We design both layers in tandem.
Can you help with real-time data requirements (WebSockets, Kafka)?
Yes. For real-time data pipelines we use Kafka or RabbitMQ for event streaming, Redis Pub/Sub for low-latency notifications, and WebSocket APIs for live dashboard updates. We've built IoT telemetry pipelines handling thousands of events per second.