Real-time Analytics API
A scalable API for collecting, processing, and visualizing real-time analytics data with support for custom event tracking and dashboards.
Completed
2022
Category
Data Processing
Team Size
3 developers
Project Overview
This Data Processing project was designed to solve complex challenges in scalability and performance. The system architecture was carefully planned to handle high traffic loads while maintaining responsiveness and data integrity.
One of the key technical challenges was implementing an efficient caching strategy that would reduce database load while ensuring data consistency. We solved this by using a multi-layered caching approach with Redis for hot data and implemented cache invalidation patterns.
The project was deployed using a containerized approach with Docker and Kubernetes, allowing for seamless scaling and deployment across multiple environments. Continuous integration and deployment pipelines were set up to ensure code quality and rapid iteration.
Key Features
Custom Event Tracking
Flexible schema for tracking any type of user or system event.
Real-time Processing
Processes and aggregates data in real-time with minimal latency.
Time-series Analysis
Optimized storage and querying for time-series data with automatic partitioning.
WebSocket Streaming
Pushes real-time updates to connected clients for live dashboards.
Technical Details
Primary Language
Python
Database
TimescaleDB
Architecture
Event-driven
Deployment
AWS ECS
Testing
Pytest
CI/CD
CircleCI