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ARCHITECTURE

🔧 Microservices

Scalable, maintainable distributed systems that grow with your business

8+ Years Experience
25+ Projects Delivered
✓ Available for new projects
Used at:
Anaqua Flowrite The Virtulab OPERR Technologies Spiio Sparrow Intelligence

$ cat services.json

Architecture Design

Design microservices architecture tailored to your business needs.

Deliverables:
  • Domain analysis and service boundaries
  • API design and contracts
  • Data ownership strategy
  • Communication patterns
  • Architecture documentation

Monolith Decomposition

Systematically migrate from monolith to microservices.

Deliverables:
  • Strangler fig strategy
  • Service extraction planning
  • Data migration approach
  • Incremental rollout
  • Rollback planning

Event-Driven Systems

Design systems that communicate through events for loose coupling.

Deliverables:
  • Event schema design
  • Message broker selection
  • Event sourcing implementation
  • Saga patterns for transactions
  • Dead letter handling

$ man microservices

When to Use Microservices

Microservices aren’t always the answer. I help you decide:

Consider microservices when:

  • Multiple teams need independent deployment
  • Different services have different scaling needs
  • Parts of the system need different tech stacks
  • You’re hitting development velocity limits

Stick with monolith when:

  • Small team (< 5 developers)
  • Still finding product-market fit
  • Simple, well-understood domain
  • Operational complexity is a concern

My Microservices Principles

  1. Single Responsibility: Each service owns one business capability
  2. Data Ownership: Services own their data, share through APIs
  3. API First: Design contracts before implementation
  4. Failure Tolerance: Design for partial failure scenarios
  5. Observability: Logging, tracing, metrics from day one
  6. Automation: CI/CD, infrastructure as code

$ cat README.md

Microservices Architecture Patterns

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┌─────────────────────────────────────────────────────────────┐
│                      API Gateway                             │
│              (Authentication, Rate Limiting, Routing)        │
└─────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│   User Service │   │ Document Svc  │   │   AI Service  │
│  (Auth, Profile)│  │ (CRUD, Search)│   │ (RAG, Agents) │
└───────────────┘   └───────────────┘   └───────────────┘
        │                     │                     │
        └──────────┬──────────┴──────────┬──────────┘
                   ▼                     ▼
        ┌───────────────┐     ┌───────────────────┐
        │  Message Bus  │     │  Event Store      │
        │   (Kafka)     │     │  (Audit, Events)  │
        └───────────────┘     └───────────────────┘

Communication Patterns I Implement

PatternUse CaseExample
Sync RESTSimple request/responseUser authentication
Async EventsLoose coupling, eventual consistencyOrder placed → Notify inventory
gRPCHigh-performance internal callsService-to-service in same cluster
SagaDistributed transactionsMulti-service checkout flow
CQRSRead/write separationSearch service + write service

Technology Stack for Microservices

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# Service Definition (Kubernetes)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: document-service
  labels:
    app: document-service
    version: v2.1.0
spec:
  replicas: 3
  selector:
    matchLabels:
      app: document-service
  template:
    spec:
      containers:
      - name: document-service
        image: registry/document-service:v2.1.0
        ports:
        - containerPort: 8080
        env:
        - name: KAFKA_BROKERS
          valueFrom:
            configMapKeyRef:
              name: kafka-config
              key: brokers
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-secrets
              key: document-db-url
        resources:
          requests:
            memory: "256Mi"
            cpu: "100m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        readinessProbe:
          httpGet:
            path: /health/ready
            port: 8080
          initialDelaySeconds: 5
        livenessProbe:
          httpGet:
            path: /health/live
            port: 8080
          initialDelaySeconds: 10

Observability Stack

For microservices to be manageable, observability is essential:

  • Logging: Structured logs with correlation IDs (ELK, Loki)
  • Tracing: Distributed request tracing (Jaeger, OpenTelemetry)
  • Metrics: Service health and business KPIs (Prometheus, Grafana)
  • Alerting: Proactive issue detection (PagerDuty, Opsgenie)

Experience:

Case Studies: FinTech Microservices | Real-time NEMT Dispatch | Real-time EdTech Platform

Related Technologies: Docker/Kubernetes, Kafka, Spring Boot, Python

$ ls -la projects/

AI Platform Services

@ Anaqua (RightHub)
Challenge:

Integrate AI capabilities across a monolithic IP management platform.

Solution:

Designed AI as separate microservices with clear API contracts, allowing independent scaling and deployment while integrating with existing systems.

Result:

AI services could evolve independently, enabling rapid feature development.

Real-Time Dispatch System

@ OPERR Technologies
Challenge:

Build system handling real-time dispatch, GPS tracking, billing, and compliance.

Solution:

Event-driven microservices with Kafka for real-time events, separate services for dispatch, tracking, billing, and reporting.

Result:

First licensed NYC dispatch provider, handling thousands of daily operations.

IoT Data Platform

@ Spiio
Challenge:

Process data from 1,000+ sensors with different ingestion, processing, and serving requirements.

Solution:

Microservices for ingestion (high throughput), processing (compute-intensive), and serving (low latency) with appropriate scaling for each.

Result:

10x traffic growth without architecture changes.

$ diff me competitors/

+ 8+ years building production distributed systems
+ Pragmatic approach—right-sized architecture, not over-engineering
+ Event-driven expertise—Kafka, RabbitMQ, event sourcing
+ Full implementation capability—not just diagrams
+ AI service integration—microservices for ML/LLM workloads

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