Skip to content

Architecture: Technology Stack (Implementation Guide)

This document mandates the specific technology choices for implementing FintraOS. These choices are designed to support the High-Throughput, Event-Sourced, and Bi-Modal Intelligence requirements of the platform.

1. Core Runtime & Language

  • Language: Go (Golang)
    • Rationale: Superior concurrency model (goroutines) for high-throughput event processing. Strong typing ensures reliability for financial data. Single binary deployment simplifies operations.
    • Usage: All microservices (Connect, Core, Guard, Pulse, Vault, Views).
  • Frontend: Next.js (React)
    • Rationale: Server-Side Rendering (SSR) for SEO and performance. React ecosystem for component reusability.
    • Usage: Developer Dashboard, Admin Console, Demo Apps.

2. Data Persistence (Polyglot Persistence)

We use the right database for the specific workload (CQRS).

A. The "Hot" Store (Transactional)

  • Technology: PostgreSQL 16
  • Role: The "Source of Truth" for current state.
  • Usage: User profiles, account metadata, current balances, active permissions.
  • Configuration: Strict ACID compliance.

B. The "Warm" Store (Time-Series & Events)

  • Technology: TimescaleDB (Postgres Extension)
  • Role: High-volume storage for the Immutable Event Log and Transaction History.
  • Usage: Storing billions of TransactionCreated events, historical balances, and metric points.
  • Rationale: Automatic partitioning, compression, and rapid time-range queries.

C. The "Cold" Store (Archival & Analytics)

  • Technology: AWS S3 (Parquet Format)
  • Role: Long-term data lake for "Slow Brain" batch processing.
  • Usage: Training data for ML models, regulatory archives (7+ years).

D. The "Read" Store (Projections)

  • Technology: Redis / CDN
  • Role: Serving pre-computed JSON views to the frontend (Millisecond latency).
  • Usage: The Views module pushes ready-to-consume JSON here.

3. Messaging & Event Bus

  • Technology: Kafka (or Redpanda for simpler ops)
  • Role: The central nervous system.
  • Usage: All inter-service communication is asynchronous via topics (e.g., core.events.v1, brain.insights.v1).
  • Pattern: "Smart Endpoints, Dumb Pipes."

4. Intelligence & ML

  • Inference: Python (FastAPI) + ONNX Runtime
    • Rationale: Python ecosystem for ML is unbeatable. ONNX provides a standardized, high-performance runtime for production.
  • Vector Database: Qdrant
    • Usage: Storing embeddings for Merchant Entity Resolution (e.g., matching "Starbucks" text to the Starbucks entity).

5. Infrastructure

  • Containerization: Docker
  • Orchestration: Kubernetes (K8s)
  • CI/CD: GitHub Actions