Real-Time Analytics Platform

January 10, 2024 • Built with: TypeScript, Apache Kafka, ClickHouse, Next.js

Data Engineering Analytics TypeScript Kafka
View on GitHub Live Demo

A real-time analytics platform that processes and visualizes millions of events per second, providing instant insights for business intelligence.

Problem Statement

Traditional analytics systems have significant lag between data collection and insights. This platform delivers real-time analytics with sub-second latency.

Core Features

  • Real-Time Ingestion: Process 5M+ events/second
  • Interactive Dashboards: Sub-second query response times
  • Custom Metrics: Define and track business-specific KPIs
  • Alerting: Real-time anomaly detection and notifications
  • Data Export: Export data to multiple formats (CSV, JSON, Parquet)

Technology Stack

Data Pipeline

  • Ingestion: Apache Kafka for event streaming
  • Processing: Apache Flink for stream processing
  • Storage: ClickHouse for OLAP queries
  • Cache: Redis for hot data

Frontend

  • Framework: Next.js with TypeScript
  • Visualization: D3.js, Recharts
  • State Management: Zustand
  • Styling: Tailwind CSS

Key Achievements

  • Reduced query latency from minutes to milliseconds
  • Handles 10x traffic spikes without degradation
  • 99.95% data accuracy
  • Saved $100k/year in infrastructure costs vs. commercial solutions

Architecture Decisions

Why ClickHouse?

  • 100x faster than PostgreSQL for analytical queries
  • Excellent compression (10:1 ratio)
  • Native support for time-series data

Event-Driven Design

  • Decoupled components for better scalability
  • Easy to add new data sources
  • Fault tolerance through event replay

Performance Optimizations

  1. Query Optimization: Materialized views for common queries
  2. Data Partitioning: Time-based partitioning for efficient queries
  3. Caching Strategy: Multi-level caching (browser, CDN, Redis)
  4. Batch Processing: Aggregate events before storage

Challenges

  • Data Quality: Implemented validation and deduplication
  • Scalability: Auto-scaling based on event throughput
  • Cost Management: Tiered storage (hot/warm/cold)

Impact

  • Enabled real-time decision making for 200+ users
  • Identified $500k in optimization opportunities
  • Improved customer retention by 15% through early insights