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This article details the improvements made to the Venice ingestion pipeline at LinkedIn, which now handles over 230 million records per second. It covers key optimizations, challenges with diverse workloads, and strategies for enhancing performance, particularly in bulk loading and active-active replication scenarios.
The article outlines six indicators that suggest an experiment should be repeated, such as solid impact results, almost significant p-values, and cases where initial results seem "too good to be true." It emphasizes the importance of revisiting past experiments for better insights and improving statistical power.