Database Architecture and Performance

Designing Data Systems That Scale

A database is only as good as its data model. I have designed data architectures for trading platforms, real-time analytics systems, and multi-tenant applications — choosing the right database for each workload and structuring schemas so queries stay fast as data grows from thousands of rows to millions.

What This Means for Your Business

The most expensive database mistake is choosing the wrong tool for the workload. PostgreSQL excels at relational queries and transactions. MongoDB handles flexible document schemas and horizontal scaling. Redis and KeyDB give you sub-millisecond reads for hot data. Most applications need more than one — and the architecture of how they interact determines whether your system scales or becomes a bottleneck.

If your application is slowing down as data grows, your queries are hitting timeouts, or you are throwing hardware at a problem that needs architecture — the fix is in the data model and access patterns, not the infrastructure bill.

How I Have Used This in Production

  1. Trading Platform Data Layer

    Bitsten exchange

    Designed the data architecture for a live crypto exchange — KeyDB for order book state requiring sub-millisecond access, RabbitMQ for reliable order ingestion, and persistent storage for trade history and OHLCV candle data. The challenge was optimizing for both high-frequency writes from the matching engine and analytical reads for charting.

  2. Trading Analytics Platform

    Personal Project — Trading Statement

    Built tradingstatement.io on PostgreSQL and AWS — a fintech analytics platform where traders upload statements and get performance breakdowns. Designed the schema for time-series trade data with efficient aggregation queries for P&L calculations, win rates, and drawdown analysis across configurable time windows.

  3. Community Learning Platform

    Personal Project — Skillcrucial

    Built the data layer for an online coding school serving 3,000+ users on MongoDB with Redis caching. MongoDB was chosen for flexible task definitions that evolve over time. Redis handles session state and leaderboard computation — sorted sets give O(log N) ranking updates as students submit solutions.

Technologies

PostgreSQLMongoDBRedisKeyDBRabbitMQDatabase DesignData ModelingAWS

A matching engine generates the most demanding write pattern in a trading system. See how I designed the engine that produces this data stream.

Building Matching Engines — Sub-Millisecond Order Processing

Hot data lives in Redis and KeyDB. Cold data lives in PostgreSQL and MongoDB. The real-time layer bridges them. See how I built that pipeline.

Real-Time Systems — WebSockets, Message Queues, and Live Data

The frontend queries the data layer through APIs designed for specific access patterns. See how I built the trading UI that consumes this architecture.

Frontend Architecture for Financial Systems — When Every Frame and Every Millisecond Counts

Application slowing down as data grows?

The fix is almost never more hardware — it is the data model. I have designed polyglot data architectures where each database serves the workload it is best at. If your queries are hitting timeouts or your infrastructure bill is growing faster than your users, let's talk architecture.

Discuss your data architecture