It is very difficult to make a choice between star and snowflake data warehouse schema. The topic is part of the broader dilemma in the data warehousing community: which approach to use, Kimball's or Inmon's. There are advocates for each approach, with fierce “war” still going on. However, very few empirical studies exist giving either side an advantage; in the past, the approaches were being selected based on the organizational, resource or goal-specific parameters. The goal of this case study was to examine which implementation of data warehouse will yield better results in the observed scenario – a data warehouse for monitoring of energy consumption in public buildings, from perspective of the ETL process. We implemented two versions of DW, one based on star and the other on the snowflake schema model, and measured the performance of the ETL process. Our goal was to find out if there is a difference in duration between two implementations, and if the difference exists, how it changes with increase of data in operational database. Series of tests were conducted to evaluate implemented solutions. Statistical analysis showed that, for the observed scenarios, implementation based on snowflake schema performs better: the ETL execution time is shorter and the size of DW is smaller. An important observation is that the target data warehouse size is linearly dependent on the amount of the operational data.
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