Global Event Tracking System | Pawan Kumar - Principal Software Developer

About This Project

A highly scalable, real-time event processing and analytics platform that ingests, processes, and analyzes millions of events per second from diverse sources across the globe. Built for enterprise customers who need real-time insights with guaranteed message delivery and sub-100ms latency. The system handles event ingestion, enrichment, transformation, storage, and real-time dashboarding with automatic failover and disaster recovery.

Technologies Used

📨 Apache Kafka 🔥 Apache Spark 🔍 Elasticsearch 📦 AWS S3 🟢 Node.js ❤️ Redis Cache 🍃 MongoDB 🔌 WebSockets

Key Features

Real-time event ingestion from multiple sources (APIs, webhooks, SDKs) at petabyte scale

Message queuing with Apache Kafka ensuring zero data loss

Stream processing with Apache Spark for complex transformations

Distributed caching with Redis for sub-100ms query responses

Full-text search capabilities with Elasticsearch for log and event search

Automatic data archival to AWS S3 for cost-effective long-term storage

Real-time dashboards with WebSocket connections for live updates

Custom event schema validation and schema evolution handling

Multi-tenant isolation with role-based access control

Comprehensive monitoring, alerting, and health checks with custom metrics

Challenges & Solutions

Challenge 1: Handling Petabyte Scale Data

Processing millions of events per second required distributed architecture spanning multiple data centers. We implemented a multi-node Kafka cluster, distributed Spark processing, and sharding strategies to handle exponential data growth without performance degradation.

Challenge 2: Guaranteed Message Delivery

Ensuring zero data loss during node failures and network partitions required idempotent processing, distributed transactions, and replication. We implemented custom failover logic and cross-region replication for disaster recovery.

Challenge 3: Sub-100ms Latency Requirement

Achieving sub-100ms latency for analytics queries at petabyte scale required intelligent caching strategies. We implemented multi-level caching (memory, Redis, columnar storage), materialized views, and query optimization.

Challenge 4: Multi-Tenancy and Security

Isolating tenant data while sharing infrastructure efficiently required careful architecture. We implemented query-time tenant filtering, encrypted storage per tenant, and role-based access control with audit logging.

Architecture & Design

The system uses a Lambda architecture combining batch and stream processing. Apache Kafka acts as the central event bus with multi-region replication. Apache Spark Streaming processes events in real-time, enriches them with contextual data, and stores results in Elasticsearch for analytics. MongoDB stores normalized event data. Redis caches frequently accessed data. AWS S3 archives historical data. Node.js APIs provide event ingestion and query endpoints. WebSockets enable real-time dashboard updates. The entire system is containerized with Docker and orchestrated via Kubernetes for auto-scaling.

Results & Impact Metrics

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1M+ Events/Second

System reliably processes over 1 million events per second with 99.99% uptime

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Sub-100ms Latency

Analytics queries return results in under 100ms even at petabyte scale

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Zero Data Loss

100% message delivery guarantee with automatic failover and cross-region replication

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60% Cost Reduction

Intelligent archival and compression reduced storage costs by 60% vs competitors

Key Learnings & Insights

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Petabyte scale requires distributed thinking—single-node optimizations don't matter at this scale

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Caching strategy is more important than raw processing power—multi-level caching is essential

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Message ordering vs throughput tradeoff—often you need to sacrifice strict ordering for speed

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Monitoring at scale is critical—you need observability built in from day one

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Multi-tenancy isolation must be enforced at every layer—security can't be added later

This is a proprietary project developed for a product-based company. Code and live demos are not publicly available due to company confidentiality policies.

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