Real-time Analytics Pipeline
Streaming data architecture for instant insights
Technology Stack
Overview
Designed and implemented a real-time streaming data pipeline processing 1M+ events per minute for customer analytics and business intelligence with sub-minute latency.
Business Problem
Business needed real-time visibility into customer behavior, system performance, and operational metrics, but existing batch processes had 24-hour delays. Critical business decisions were being made with stale data, leading to missed opportunities and reactive rather than proactive responses.
Approach & Solution
Built a streaming architecture using Spark Structured Streaming with Delta Lake for reliability, implemented automated monitoring and alerting, and created real-time dashboard updates. Used event-driven architecture with proper schema evolution and data quality checks.
Challenges Overcome
Handling out-of-order events in distributed systems, ensuring exactly-once processing semantics, maintaining sub-minute latency at scale, implementing robust error handling and recovery, and managing schema evolution without breaking downstream consumers.
Results & Impact
Achieved 99.9% uptime with <30 second end-to-end latency, enabling real-time business decisions. Processed over 1M events per minute during peak loads and reduced data infrastructure costs by 25% through optimized resource utilization.
Demonstrates expertise in streaming architectures, distributed systems, data reliability engineering, and performance optimization at enterprise scale with mission-critical uptime requirements.
Key Highlights
Quick bullets for recruiters and hiring managers:
- 1M+ events processed per minute at peak load
- Sub-30 second end-to-end latency maintained
- 99.9% uptime with automated failover and recovery
- Real-time business intelligence and operational dashboards
- 25% reduction in infrastructure costs through optimization