There are several pitfalls of using BigQuery over a self-hosted on-prem data warehousing solution, particularly when it comes to performance, cost, and complexity.

In terms of performance, BigQuery can be slower than an on-prem solution when it comes to complex queries that require significant processing power. This is because BigQuery is a shared resource, and the processing power available to a user can be limited depending on the number of concurrent users and the complexity of their queries. On the other hand, an on-prem solution can be optimized for specific workloads and can provide better performance for complex queries.

When it comes to cost, BigQuery can be more expensive than an on-prem solution for large volumes of data. BigQuery charges based on the amount of data scanned by a query, which can quickly add up for large datasets. In addition, BigQuery charges for storage, which can be more expensive than on-prem storage solutions. However, for small to medium sized datasets, BigQuery can be more cost-effective than an on-prem solution due to its pay-as-you-go pricing model.

Finally, the complexity of setting up and maintaining an on-prem solution can be a significant challenge for many organizations. This includes hardware and software installation, ongoing maintenance and upgrades, and staffing to manage the system. BigQuery, on the other hand, is a fully managed cloud solution that requires minimal setup and maintenance.

Overall, the choice between BigQuery and an on-prem solution depends on a variety of factors, including the size of the dataset, the complexity of the queries, and the resources available to set up and manage the system. While BigQuery can be a cost-effective and easy-to-use solution for small to medium sized datasets, it may not be the best choice for larger or more complex datasets that require significant processing power or on-prem storage solutions.
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