Case Study

Real-Time Analytics & Event Pipeline

Developing a high-throughput event processing pipeline to ingest and analyze millions of data points daily, providing real-time visibility into system performance.

Role
Systems Architect
Timeline
5 Months
Industry
Data Intelligence / AdTech
Focus
Apache Kafka

Problem Breakdown

The client was relying on batch processing that took several hours to generate reports, making it impossible to react to real-time traffic anomalies or performance degradations.

Architecture Decisions

  • /Apache Kafka for durable event buffering and replayability
  • /ClickHouse for high-performance analytical queries and storage efficiency
  • /Go-based stream processors for low-latency enrichment

Trade-offs

  • ¬Operational complexity of managing a Kafka and ClickHouse cluster
  • ¬Increased cost of real-time architectural components
  • ¬Eventual consistency in reports due to stream processing delays

Key Outcomes

  • Handled sustained loads of 50 million events per day.
  • Reduced time-to-insight from 4 hours to less than 1 second.
  • Enabled real-time anomalous traffic detection and alerting.
  • Lowered database storage costs by 50% using ClickHouse compression.
Apache KafkaClickHouseGoRedisVector

Have a similar system challenge?

I specialize in solving high-stakes technical problems for founders. Let's build something scalable together.

Book a technical discovery call 

Typically respond within 24 hours