Building Marketcap.guide: Real-time Crypto Data Architecture

Share the technical details of building your crypto data platform - from data sources to serving users.

Project Overview and Requirements

What to write about:

What marketcap.guide provides to users
Performance and reliability requirements
Scale considerations (number of tokens, update frequency, users)
Competitive landscape and differentiation
Technical constraints and budget considerations

Explain what you built and why it required a sophisticated data architecture.

Data Source Strategy

What to write about:

Which APIs and data providers you chose
Evaluation criteria for data sources
Handling multiple sources and data conflicts
API rate limiting and cost management
Backup strategies for data source failures
Data quality and validation approaches

Discuss how you sourced and validated cryptocurrency market data.

Real-time Data Ingestion Pipeline

What to write about:

Architecture for ingesting live price feeds
Technologies used (WebSockets, message queues, etc.)
Handling different update frequencies across tokens
Batching vs real-time processing trade-offs
Error handling and retry mechanisms
Monitoring data ingestion health

Explain how you built the data ingestion system to handle continuous updates.

// Example of WebSocket data ingestion
const priceSocket = new WebSocket('wss://api.exchange.com/v1/stream');

priceSocket.on('message', (data) => {
    const priceUpdate = JSON.parse(data);
    
    // Validate and process price update
    if (isValidPriceUpdate(priceUpdate)) {
        updateQueue.push({
            symbol: priceUpdate.symbol,
            price: priceUpdate.price,
            timestamp: Date.now(),
            volume: priceUpdate.volume
        });
    }
});

Database Design and Storage Strategy

What to write about:

Database technology choices (SQL vs NoSQL, time-series DBs)
Schema design for price history and metadata
Indexing strategies for fast queries
Data retention and archival policies
Backup and disaster recovery
Scaling database reads and writes

Detail your database architecture and how you optimized for both writes and reads.

Caching and Performance Architecture

What to write about:

Multi-layer caching strategy
Cache invalidation and consistency challenges
CDN usage for global performance
In-memory vs distributed caching
Cache warming strategies
Performance monitoring and optimization

Explain how you achieved fast response times for users worldwide.

API Design and Rate Limiting

What to write about:

REST API design principles you followed
Rate limiting implementation and strategy
Authentication and API key management
Response format optimization
Error handling and status codes
API versioning strategy

Share how you designed the public API for developers and applications.

Frontend Performance and Real-time Updates

What to write about:

Technology stack for the frontend
Real-time price updates to users
Optimizing for mobile performance
State management for thousands of tokens
Progressive loading and lazy loading
SEO considerations for crypto data

Discuss the frontend challenges of displaying real-time financial data.

Monitoring, Alerting, and Observability

What to write about:

Key metrics you track (latency, accuracy, uptime)
Alerting for data quality issues
Performance monitoring across the stack
Log aggregation and analysis
Error tracking and debugging
Business metrics and user analytics

Explain how you monitored the health and performance of your data pipeline.

Scaling Challenges and Solutions

What to write about:

Traffic spikes during market volatility
Database scaling strategies
Auto-scaling infrastructure decisions
Cost optimization techniques
Performance bottlenecks you discovered
Architecture evolution as you grew

Share specific scaling challenges and how you solved them.

Data Quality and Accuracy

What to write about:

Detecting and handling bad data
Cross-validation between data sources
Handling market manipulation and flash crashes
Data audit trails and corrections
User reporting of data issues
Regulatory and compliance considerations

Discuss how you ensured data accuracy and reliability.

Lessons Learned and Architecture Evolution

What to write about:

Major architectural decisions you'd change
Technologies that worked well vs disappointments
Unexpected challenges and solutions
Cost vs performance trade-offs
What you learned about crypto market data
Future improvements and features

Reflect on the technical journey and what you'd do differently.

Include architecture diagrams, performance metrics, and code examples. Share specific numbers around throughput, latency, and scale to make this a practical reference for others building similar systems.