Capacity planning is a problem for a data warehouse because it sets contrasting functional requirements against each other. On one hand, data warehouse customers consume data warehouse capacity as they query the data in the data warehouse business intelligence (BI) reporting. Meanwhile, applications consume data warehouse capacity as they load data into a data warehouse through the extract, transform and load (ETL) process. These two functions, BI reporting and ETL, grow in volume and frequency as a data warehouse grows. Additionally, database administrator (DBA) tasks, such as backups and table reorganizations can cause additional data warehouse bottlenecks because ETL, BI reporting and DBA tasks contend for the same resources.  

Often, the first solution proposed for resource contention is more resources. However, such contention for system resources can marginalize the value and impact of additional hardware and processing capacity. To paraphrase an old saying, we don’t need more cooks in the kitchen, and we don’t need more kitchen for the cooks.

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