RoleFerry's data architecture is designed to handle complex recruitment data with high performance, scalability, and data integrity.
📊 Data Flow Architecture
graph TB
subgraph "Data Sources"
A[Resume Uploads]
B[Job Descriptions]
C[User Profiles]
D[External APIs]
end
subgraph "Processing Layer"
E[AI Analysis]
F[Data Validation]
G[Match Scoring]
H[Content Generation]
end
subgraph "Storage Layer"
I[PostgreSQL]
J[Redis Cache]
K[File Storage]
L[Search Index]
end
subgraph "Output Layer"
M[Email Campaigns]
N[Analytics Dashboard]
O[API Responses]
P[Reports]
end
A --> E
B --> E
C --> F
D --> F
E --> G
F --> G
G --> H
H --> I
I --> J
I --> K
I --> L
J --> M
K --> N
L --> O
I --> P
📊 Data Flow Architecture
Data Ingestion
Real-time data collection from resumes, job postings, and user interactions
Data Processing
AI-powered parsing, analysis, and enrichment of recruitment data
Data Storage
Multi-tier storage with PostgreSQL, Redis, and S3 for different data types
Data Analytics
Real-time analytics and reporting for recruitment insights