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EA

Event-Driven Analytics Platform

Data Platform
Client: Internal Project
Period: 2023 - Present

A high-throughput event processing platform that ingests, processes, and analyzes millions of events in real-time. The platform uses event sourcing, CQRS, and stream processing to provide real-time analytics and insights. It supports multiple event sources, provides real-time dashboards, and enables complex event processing patterns. The system is designed for horizontal scalability and high availability.

1M+
Events/Day
<500ms
Processing Latency
99.9%
Uptime

Problem Statement

Organizations needed to process and analyze high volumes of events in real-time to enable data-driven decision making.

Architecture & Technical Approach

Azure Event Hubs for ingestion. Azure Stream Analytics for processing. .NET services for custom processing. Redis for caching. Azure Data Lake for storage. Power BI for visualization.

Challenge

Handling event spikes and maintaining low latency while processing millions of events daily

Solution

Implemented auto-scaling, message batching, optimized processing pipelines, and efficient data storage strategies

Impact

Enabled real-time decision making, improved operational efficiency, and provided actionable insights from event data

Key Features

Real-time event ingestion and processing
Stream analytics and aggregations
Event sourcing and replay capabilities
Real-time dashboards and alerts
Scalable architecture with auto-scaling
Data retention and archival
Complex event processing
Multi-source event integration

Technologies & Tools

Azure Event Hubs
Azure Stream Analytics
.NET
Kafka
Redis
Power BI
Azure Data Lake
Azure Functions

Lessons Learned

Event-driven architecture enables real-time capabilities
Auto-scaling is essential for variable workloads
Batching improves throughput
Event sourcing provides valuable audit capabilities