文章导读 在云原生架构逐渐成为主流的今天，越来越多企业开始将 Apache Kafka 与数据湖、Lakehouse 结合，构建实时数据分析能力。然而，Kafka 到 Iceberg 的这条链路却并不轻松：需要依赖 Flink、Spark 等复杂 ETL 工具，开发与运维成本都不低。 有没有更简单的解法？

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The first step to sizing or scaling Kafka for optimal cost and performance is understanding how the data streaming platform uses resources. Here’s a primer. Teams implementing Apache Kafka, or ...