Database research and development is heavily influenced by benchmarks, such as the industry-standard TPC-H and TPC-DS for analytical systems. However, these 20-year-old benchmarks capture neither how databases are deployed nor what workloads modern cloud data warehouse systems face. In this paper, we summarize well-known, confirm suspected, and unearth novel discrepancies between TPC-H/DS and actual workloads using empirical data. We base our analysis on telemetrics from Amazon Redshift, one of the largest cloud data warehouse deployments. Among other insights, we show how write-heavy data pipelines are prominent, workloads vary over time (in both load and type), queries are repetitive, and most properties of queries or workloads experience very long-tailed distributions. We conclude that data warehouse benchmarks, just like database systems, need to become more holistic and stop focusing solely on query engine performance. Finally, we publish a dataset containing query statistics for 200 randomly selected Redshift serverless and provisioned instances (each) over a three-month period, as a basis for building more-realistic benchmarks.
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