Why is it recommended to avoid copying data unnecessarily between datasets in BigQuery?

The recommendation to avoid copying data unnecessarily between datasets in BigQuery is primarily due to its impact on cost and performance. While complicating dataset management is a valid concern, the most notable consequence of unnecessary data duplication is indeed related to cost.

BigQuery operates under a pricing model that charges for the storage of data as well as the queries run on that data. When data is copied unnecessarily, it increases the total storage costs because you end up paying for multiple copies of the same data. Additionally, having multiple copies can lead to inefficiencies during querying, as queries may need to scan multiple datasets rather than targeting a single source of truth.

Moreover, unnecessary duplication can lead to issues with data consistency and integrity. If data is updated in one dataset but not in another, it raises the risk of discrepancies. This ties back into the management aspect, as maintaining multiple copies requires extra administrative overhead to ensure that all datasets are synchronized and up-to-date.

In summary, while complicating dataset management is a valid concern, the most significant factor in the context of BigQuery is the financial impact and potential for inconsistencies that arise from unnecessary data duplication.