The field of database research is moving towards a more dynamic and realistic evaluation of database components, with a focus on modeling and generating data and workload drift. This shift is driven by the need for more accurate and reliable benchmarking, as existing static benchmarks are limited in their ability to capture real-world scenarios. Researchers are exploring new approaches to benchmarking, including the use of microsecond-latency memory and the analysis of performance variability in cloud environments. Noteworthy papers include DriftBench, which proposes a unified taxonomy for data and workload drift and introduces a lightweight framework for generating drift in benchmark inputs. Another notable paper analyzes the impact of microsecond-level memory latency on key-value store performance and finds that software prefetching can effectively mitigate throughput degradation.