<<<<<<< HEAD ======= >>>>>>> e2f3a67 (Rebrand)

← Back to Projects  |  View code on GitHub

======= 📄 Download Resume
>>>>>>> e2f3a67 (Rebrand)

Serverless Event Platform

Event-Driven Architecture with Azure Functions & Event Grid

From Batch Processing to Real-Time Events

1M+ Events/Day
99.9% Delivery Rate
<50ms Event Latency
70% Cost Reduction
📖 Read the Story 🏗️ See Architecture 💼 Business Impact

🎯 The Challenge: Real-Time Event Processing

Required to modernize legacy batch processing systems into a real-time, event-driven architecture using Azure serverless technologies. The goal was to process millions of events daily with sub-second latency while maintaining cost efficiency and reliability.

🚨 The Legacy Problem

  • Batch Processing Delays: Hours-long processing windows with data staleness
  • Tight Coupling: Monolithic systems with difficult-to-scale components
  • Resource Waste: Always-on infrastructure with poor utilization
  • Error Recovery: Limited fault tolerance and manual intervention required
  • Scaling Issues: Cannot handle sudden traffic spikes or varying workloads

🏗️ Event-Driven Serverless Architecture

Designed and implemented a complete serverless event platform using Azure Functions, Event Grid, Service Bus, and Cosmos DB with automated scaling, dead letter handling, and comprehensive monitoring.

⚡ Azure Functions

Serverless compute for event processing and business logic

🔗 Azure Event Grid

Event routing and delivery with reliable messaging

🚌 Azure Service Bus

Message queuing with guaranteed delivery and ordering

🌍 Azure Cosmos DB

Globally distributed NoSQL database for event storage

🔒 Azure Key Vault

Secure secrets and configuration management

📊 Application Insights

Real-time monitoring and distributed tracing

🔄 Logic Apps

Workflow orchestration and integration connectors

⏰ Azure Event Hubs

High-throughput event ingestion and streaming

🏗️ Event Platform Components

  • Event Ingestion: Event Hubs for high-volume event streaming and buffering
  • Event Routing: Event Grid with custom topics and intelligent filtering
  • Processing Functions: Azure Functions with multiple triggers and bindings
  • State Management: Cosmos DB with global distribution and consistency levels
  • Message Queuing: Service Bus with dead letter queues and retry policies
  • Monitoring & Observability: Application Insights with custom metrics and alerting

⚡ Event-Driven Platform Features

📈 Auto-Scaling

  • Dynamic scaling based on event volume
  • Consumption-based pricing model
  • Zero infrastructure management
  • Burst capacity for traffic spikes

🔄 Event Processing

  • Real-time event streaming and filtering
  • Custom event schemas and validation
  • Event replay and reprocessing capabilities
  • Batch and stream processing modes

🛡️ Reliability & Recovery

  • Guaranteed at-least-once delivery
  • Dead letter queue handling
  • Automatic retry with exponential backoff
  • Circuit breaker patterns

🔗 Integration Ecosystem

  • 200+ built-in connectors
  • Custom API integration
  • Third-party service webhooks
  • Legacy system integration

📊 Monitoring & Insights

  • Real-time event metrics and dashboards
  • Distributed tracing across functions
  • Custom alerting and notifications
  • Performance optimization insights

🔒 Security & Governance

  • Azure AD authentication and authorization
  • Event payload encryption
  • Compliance and audit logging
  • Network isolation and private endpoints

🎯 Real-World Business Impact

1M+ Events Per Day
<50ms Processing Latency
99.9% Event Delivery
70% Cost Reduction

💼 Transformation Story

😤 Before Event Platform

  • Batch processing with 4-6 hour delays
  • Always-on infrastructure with high costs
  • Manual scaling and resource management
  • Limited fault tolerance and error recovery
  • Tight coupling between system components

🚀 After Serverless Implementation

  • Real-time processing with sub-second latency
  • Pay-per-use model with 70% cost reduction
  • Automatic scaling based on event volume
  • Built-in resilience with retry and dead letter handling
  • Loosely coupled microservices architecture

🎉 Success Metrics

Performance: Sub-50ms event processing with 99.9% delivery rate
Scalability: Handles 10x traffic spikes without manual intervention
Cost Efficiency: 70% cost reduction through serverless consumption model
Reliability: Zero data loss with automatic error recovery

⚙️ Technical Implementation Details

🎯 My Role as Serverless Architect & Event Engineer

  • Event Architecture: Designed event schemas, routing patterns, and processing workflows
  • Function Development: Created Azure Functions with optimal triggers and bindings
  • Message Design: Implemented reliable messaging patterns with Service Bus and Event Grid
  • State Management: Configured Cosmos DB with appropriate consistency and partitioning
  • Monitoring Setup: Application Insights integration with custom metrics and alerts
  • Error Handling: Dead letter queues, retry policies, and circuit breaker implementations

🔧 Key Technologies & Event Patterns

Event Streaming

Event Hubs for high-throughput ingestion with Event Grid routing

Serverless Compute

Azure Functions with multiple triggers and auto-scaling

Data Storage

Cosmos DB with global distribution and optimized partitioning

Integration

Logic Apps for workflow orchestration and external integrations

📋 Implementation Workflow

  1. Event Design: Schema definition and event sourcing patterns
  2. Infrastructure Setup: Azure resource provisioning with ARM templates
  3. Function Development: Event processing logic with optimal performance
  4. Event Routing: Event Grid configuration with filtering and subscription
  5. State Management: Cosmos DB setup with consistency and partitioning
  6. Error Handling: Dead letter queues and retry policy implementation
  7. Monitoring Integration: Application Insights with custom dashboards
  8. Performance Testing: Load testing and capacity planning

💡 Share this story: LinkedIn | Twitter | Email
Help others discover how serverless event-driven architecture transforms real-time processing

<<<<<<< HEAD ======= >>>>>>> e2f3a67 (Rebrand)