Scaling Apache Kafka Consumers for High Throughput with Proxy or Client Library for API and Database Integration
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Scaling Kafka Consumers: Proxy vs. Client Library for High-Throughput Architectures

Apache Kafka’s pull-based model and decoupled architecture offer unmatched flexibility for event-driven systems. But as data volumes and consumer applications grow, new challenges emerge; from head-of-line blocking and rising operational overhead to complex failure handling. This post explores real-world lessons from companies like Wix and Uber, highlighting common consumer scalability issues and two main solutions: push-based consumer proxies and enhanced client libraries like Confluent’s Parallel Consumer. It concludes with a vision for a serverless Kafka consumption model that reduces total cost of ownership while preserving Kafka’s core strengths.
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How to do Error Handling in Data Streaming
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Error Handling via Dead Letter Queue in Apache Kafka

Recognizing and handling errors is essential for any reliable data streaming pipeline. This blog post explores best practices for implementing error handling using a Dead Letter Queue in Apache Kafka infrastructure. The options include a custom implementation, Kafka Streams, Kafka Connect, the Spring framework, and the Parallel Consumer. Real-world case studies show how Uber, CrowdStrike, Santander Bank, and Robinhood build reliable real-time error handling at an extreme scale.
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