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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Online Feature Store for AI ML with Data Streaming using Apache Kafka Flink FlinkSQL Confluent Cloud at Wix
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Online Feature Store for AI and Machine Learning with Apache Kafka and Flink

Real-time personalization requires more than just smart models. It demands fresh data, fast processing, and scalable infrastructure. This blog post explores how Wix.com rebuilt its online feature store using Apache Kafka and Flink, turning their AI architecture into a real-time powerhouse that supports personalized experiences for millions of users.
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