Data & Backend EngineeringSystem Design Phase
Real-Time Log Aggregator with Elasticsearch
A data digestion pipeline configured to intake, categorize, and structurally visualize over three million localized telemetry events per second without crashing.
Core Technology Stack
Elasticsearch
Logstash
Kibana (ELK)
Kafka
Architectural Constraints
A sudden burst in application traffic (DDoS or virality) easily overflows database write buffers, immediately shutting down entire backend layers.
System Implementation
Kafka acts as an impenetrable "shock absorber" buffering the burst natively into disk memory while the ELK stack steadily churns the backlog at mathematically sustained limits.
Infrastructure Deep Dive
Applications push unformatted raw strings directly to an Apache Kafka messaging queue. Logstash consumers physically drain the queue chronologically, parse them structurally, and map them to localized Elasticsearch shards.