Best practices for downsampling billions of rows of data

In collaboration with a major A/B testing platform, strategies were explored to enhance the efficiency of processing nearly a petabyte of data daily by employing downsampling techniques. Downsampling, the process of transforming raw data into a more compact form while retaining core characteristics, was evaluated to reduce computational resources and costs without sacrificing statistical accuracy. The approach involves selecting a representative subset of data, ensuring statistical rigor is maintained, especially critical for A/B testing. A practical example from a Tinybird customer use case demonstrated the application of a downsampling strategy, which included hashing user IDs to maintain a consistent sample size of 10%, helping to preserve user-level data sequences critical for analysis. This resulted in significant performance improvements, reducing data processing requirements while maintaining a manageable level of precision. The process involves balancing trade-offs between precision and performance and iteratively testing and optimizing sampling strategies to determine the most effective approach for specific needs.