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Scalable Data Processing System

DataFlow Analytics API

DataFlow Corp needed a scalable API system that could handle large volumes of data processing while providing real-time analytics and insights. The system needed to be highly performant, secure, and capable of integrating with various data sources and third-party services.

Timeline

5 weeks

Client

DataFlow Corp

Completed

Project Results

Measurable outcomes that demonstrate the success and impact of this project.

10hrs

Saved per week in manual data processing

99.5%

API uptime with auto-scaling

5x

Faster data processing compared to previous system

The Challenge

The main challenges were handling large-scale data processing efficiently, ensuring data security and compliance, implementing real-time analytics, and creating a scalable architecture that could grow with the business.

The Solution

I designed a microservices architecture using Node.js and Docker, implemented efficient data processing pipelines, used Redis for caching and real-time features, and deployed on AWS with auto-scaling capabilities for optimal performance and cost efficiency.

Key Features

RESTful API with OpenAPI Documentation
Real-time Data Processing Pipelines
Advanced Caching with Redis
Automated Report Generation
Microservices Architecture
Rate Limiting & Security
Data Validation & Sanitization
Comprehensive Logging & Monitoring
Third-party Integrations
Automated Testing Suite

Technologies Used

Node.js
Express
PostgreSQL
Redis
AWS
Docker
Elasticsearch

architecture

Microservices architecture using Docker containers for scalability and maintainability, with API Gateway for routing and load balancing.

database

PostgreSQL with optimized indexing and query performance, Redis for caching frequently accessed data and session management.

processing

Asynchronous data processing using job queues, with Elasticsearch for full-text search and analytics capabilities.

deployment

AWS deployment with auto-scaling groups, load balancers, and comprehensive monitoring using CloudWatch and custom metrics.

Development Process

A detailed breakdown of how this project was planned, developed, and delivered.

1

Requirements Analysis

Analyzed data flow requirements, performance needs, and integration points

1 week
Deliverables:
  • Technical requirements
  • API specification
  • Architecture design
2

Database Design

Designed optimized database schema, indexing strategy, and data relationships

3 days
Deliverables:
  • Database schema
  • Indexing strategy
  • Migration scripts
3

API Development

Built API endpoints, implemented business logic, and integrated security measures

3 weeks
Deliverables:
  • API endpoints
  • Authentication system
  • Data processing pipelines
4

Testing & Deployment

Comprehensive testing, performance optimization, and production deployment

1 week
Deliverables:
  • Test suite
  • Performance optimization
  • Production deployment
"The API architecture Aura built is incredibly robust and scales perfectly with our growing user base. The documentation is excellent, and the performance improvements have allowed us to serve 5x more clients without any issues."

Emma Rodriguez

CTO, DataFlow

A data analytics company providing business intelligence solutions to enterprise clients.

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