20 Best Practices for Working With Apache Kafka at Scale

Apache Kafka is a widely popular distributed streaming platform that thousands of companies like New Relic, Uber, and Square use to build scalable, high-throughput, and reliable real-time streaming systems. For example, the production Kafka cluster at New Relic processes more than 15 million messages per second for an aggregate data rate approaching 1 Tbps. Kafka has gained popularity with application developers and data management experts because it greatly simplifies working with data streams. But Kafka can get complex at scale. A high-throughput publish-subscribe (pub/sub) pattern with automated data retention limits doesn't do you much good if your consumers are unable to keep up with your data stream and messages disappear before they're ever seen. Likewise, you won't get much sleep if the systems hosting the data stream can't scale to meet demand or are otherwise unreliable.

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big data,scalability,kafka,kafka architecture