Architecture
Before choosing a database or queue, I write down the behavior we cannot accidentally change: who may see the data, how long a request may take, what happens after a retry, and how we recover a bad deploy. That list usually matters more than the diagram.
1. Bound the API contract, response shape, and pagination behavior before optimizing.
2. Choose persistence based on access patterns: relational, document, graph, cache, search, or stream.
3. Move slow work to queues only when retry, idempotency, and user expectations are clear.
4. Make failure modes visible through logs, health checks, metrics, and production-safe smoke checks.
Portfolio Platform
A small full-stack production platform with public pages, admin content, API, database, and deploy automation.
Layer 1
Layer 2
Layer 3
Layer 4
Before implementation
Data
I model read/write paths, ownership, indexes, and retention rules before picking PostgreSQL, MongoDB, Neo4j, Redis, or Elasticsearch.
Runtime
User-facing requests stay bounded; expensive jobs move behind queues only when retry and observability behavior is defined.
Delivery
Docker, Nginx, CI/CD, health checks, environment boundaries, and rollback notes are part of the architecture, not afterthoughts.
Team
Module boundaries, PR standards, docs, and ownership rules keep a backend team productive after the first implementation is done.
Decision notes
Concern
Decision rule
Typical tools
Transactional data
Use when correctness, relations, constraints, and predictable querying matter.
PostgreSQL, Prisma, TypeORM
Realtime delivery
Separate durable state from transport events and define what can be dropped.
Socket.IO, WebRTC, RabbitMQ
High-cost work
Move out of request path when the user can wait and retries are safe.
RabbitMQ, workers, Redis
Search and graph data
Use specialized stores when relational queries become the wrong model.
Elasticsearch, Neo4j, MongoDB
Production operations
Plan for deployment, monitoring, logs, backups, and rollback before release.
Docker, Nginx, Linux VPS, GitHub Actions
System maps
Financial backend systems with KYC/AML, exchange integrations, blockchain nodes, and polyglot persistence.
Enabled compliant onboarding and scalable backend features for financial workflows.
Open case studyExchange Backend and KYC Flow
Compliance-sensitive onboarding and exchange workflows backed by relational, document, graph, cache, and search stores.
Layer 1
Layer 2
Layer 3
Layer 4
Scaling a production NestJS backend with PostgreSQL, Redis, RabbitMQ, Prisma, and disciplined team delivery.
Maintained 99.9% uptime, reduced API latency by 40%, and supported consistent delivery with a backend team of 8 engineers.
Open case studyOperator Platform Runtime
Support, automation, and real-time workflows coordinated through bounded NestJS modules and async processing.
Layer 1
Layer 2
Layer 3
Layer 4
Gold and crypto trading backend systems with live pricing, chart history, wallets, payments, and provider/admin operations.
Shipped trading and payment backend capability for gold and crypto market flows, including live pricing, chart history, wallet transactions, gateway integrations, and production operations.
Open case studyTrading and Payment Runtime
Gold and crypto trading flows coordinated through pricing, charting, wallet, gateway, and operations services.
Layer 1
Layer 2
Layer 3
Layer 4
A consulting platform with chat, VoIP, video, learning modules, and backend microservices.
Supported 500+ concurrent users with real-time communication flows and faster SQL retrieval paths.
Open case studyReal-Time Consulting Platform
Communication state, session orchestration, SQL persistence, and media flows kept in separate runtime concerns.
Layer 1
Layer 2
Layer 3
Layer 4