Applications  Evaluate in larger scope of real datasets and query workloads  Examples: Customer workload, ML pipelines (dataprep, training, eval) Experiments and Result Presentation 7 706.015 Introduction many ways how to do it wrong  Basic Planning  Which data / data sets should be used?  Which workload / queries should be run?  Which hardware & software should be used?  Metrics: What to measure Reproducibility  Verify robustness of results wrt parameters and environments  Examples: different data and workload characteristics, hardware Reproducibility and RDM 26 706.015 Introduction to Scientific Writing