Data & Integration Platforms

Overview

Built production data systems capable of ingesting and processing large volumes of information from external systems, with emphasis on reliability and correctness.

Core Capabilities

API Ingestion

• Multi-protocol support (REST, GraphQL, webhooks)
• Rate limiting and backpressure handling
• Automatic retry with exponential backoff
• Connection pooling and timeout management

Incremental Synchronization

• Change Data Capture (CDC) pattern implementation
• Cursor-based pagination tracking
• Checkpointing and recovery
• Duplicate detection and handling

Data Transformation

• Schema mapping and validation
• Type coercion and normalization
• Custom business logic transformations
• Error isolation and dead-letter queues

Schema Evolution

• Backward compatibility handling
• Migration tracking and versioning
• Non-breaking schema updates
• Migration tooling

Retry and Failure Handling

• Exponential backoff strategies
• Circuit breaker patterns
• Dead-letter queue management
• Comprehensive error logging

Large-Scale Database Processing

• Batch operations optimization
• Connection pooling
• Transaction management
• Concurrent processing with concurrency control

Technical Architecture

Data Flow

External Systems → API Layer → Message Queue → Transform → Database
     (Various APIs)      (Webhooks)      (Kafka)      (Rules)    (PostgreSQL)
                                            ↓
                                       Error Queue
                                            ↓
                                      Dead Letter

Components

1. Ingestion Layer

   • REST API client library
   • Webhook server for push-based integrations
   • Connection retry logic
   • Request/response logging

2. Transformation Layer

   • Schema validator
   • Data normalizer
   • Business logic engine
   • Error handler

3. Storage Layer

   • PostgreSQL with optimized indexes
   • Connection pool (50-100 connections)
   • Batch insert operations
   • Transaction management

4. Monitoring

   • Event throughput metrics
   • Latency tracking (p50, p95, p99)
   • Error rate monitoring
   • Data quality checks

Key Features

• High Throughput: 10M+ events daily
• Low Latency: Sub-second incremental sync
• High Reliability: 99.99% uptime SLA
• Data Quality: 99.99% accuracy with validation
• Scalability: Handles 500+ concurrent data sources
• Observability: Complete audit trail and debugging

Implementation Patterns

Idempotency

Every operation designed to be safely retried:

• Database upserts instead of inserts
• Idempotency keys in API calls
• State tracking for retries

Data Quality

• Schema validation on ingestion
• Checksum verification
• Reconciliation runs
• Anomaly detection

Monitoring & Alerting

• Real-time metrics dashboards
• Alerts on error rate threshold
• Data freshness monitoring
• Performance regression detection

Lessons Learned

1. Make It Reliable Before Making It Clever: Focus on data correctness first, optimization second
2. Fail Fast & Explicitly: Handle errors immediately with clear error messages
3. Observability is Critical: Comprehensive logging makes debugging possible
4. Idempotency Everywhere: Always assume operations will be retried
5. Test Edge Cases: Off-by-one errors, null values, rate limits

Tech Stack

Performance Metrics

• Throughput: 10,000 events/sec sustained
• Latency: 100-500ms end-to-end
• Database: 1000+ inserts/sec
• API Response: <500ms p95
• Error Rate: <0.01%