Event Streaming & Data Architecture

I design and scale Kafka-based architectures for high-throughput and multi-cluster environments.

Problem Space

Scaling data processing to meet real-time requirements often results in architectural bottlenecks and operational latency.

Architectural Approach

Design focuses on multi-cluster Kafka patterns, schema management, and configurations that prioritize data integrity and system availability at scale.

Outcomes

  • Delivered streaming systems handling millions of events per second.
  • Reduced data processing latency for production applications.
  • Implemented disaster recovery and failover strategies for Kafka infrastructure.

Streaming Performance & Scaling

I scale Kafka consumers and clusters deterministically, without rebalance penalties or runaway cost.

Problem Space

Standard consumer group scaling introduces partition rebalances, causing latency spikes and processing stalls. High-throughput streaming systems need to scale horizontally without those penalties, and without infrastructure cost growing faster than load.

Architectural Approach

Rebalance-avoidance scaling strategies across independent consumer groups, custom scaling control planes triggered by observed throughput metrics, and environment isolation for multi-client deployments.

Outcomes

  • Achieved a ~50% infrastructure cost reduction during off-peak periods.
  • Eliminated consumer group rebalance disruptions during scaling events.
  • Sustained 120k msg/sec peak throughput with deterministic scale-up and scale-down.

Production Delivery & Reliability

I take streaming systems from prototype to durable production operation under real load.

Problem Space

POC systems stall before production, and production systems fail under burst traffic that exceeds their designed capacity.

Architectural Approach

Capacity planning against measured peak load, deterministic rollout and rollback through versioned deployments, and monitoring-driven scaling triggers.

Outcomes

  • Sustained 42M messages per minute at peak on a high-concurrency commercial system.
  • Reduced node scale-time from 15 minutes to approximately 2 minutes.
  • Reduced infrastructure cost by 40% through compute migration and right-sizing.