What are the pitfalls of using BigQuery over a self-hosted on-prem data warehousing solution? If you can include as much detail around performance, cost and complexity to setup both, that would be ideal. The most important aspect is cost implications as the current requirement is that I have a cheap solution
Google BigQuery is a fully-managed, serverless data warehouse solution that can offer some advantages over self-hosted on-prem data warehousing solutions. However, there are certain pitfalls to be aware of when choosing between these options. Here's a comparison of the two, focusing on performance, cost, and complexity:

1. Performance:
    - BigQuery: It is highly scalable and designed to handle large volumes of data and queries. However, BigQuery is not as optimized for small, frequent updates and might have slightly higher latency compared to some on-prem solutions.
    - On-prem: Depending on your hardware and infrastructure, performance may vary. For highly optimized systems, on-prem solutions can deliver lower query latency. However, scaling might be more complicated and time-consuming compared to BigQuery.
1. Cost:
    - BigQuery: BigQuery's pricing is based on a pay-as-you-go model, which includes both storage and query costs. While this can be cost-effective for unpredictable or fluctuating workloads, costs might increase rapidly as data and query volume grow. In addition, egress costs apply if you transfer large volumes of data out of BigQuery, which can significantly increase overall costs.
    - On-prem: An on-prem solution may have higher upfront costs due to hardware, software licenses, and infrastructure setup. However, the long-term costs might be lower, especially for predictable and stable workloads. You can avoid recurring costs and have more control over expenses.
1. Complexity:
    - BigQuery: One of the primary benefits of BigQuery is its ease of use and minimal setup requirements. You don't need to worry about infrastructure management, and integration with other Google Cloud services is seamless. However, there might be a learning curve if you're unfamiliar with the Google Cloud Platform.
    - On-prem: Setting up an on-prem data warehouse can be complex, time-consuming, and require significant in-house expertise. You will need to manage hardware, software, networking, and security aspects. Additionally, integration with other services and tools may require more effort compared to BigQuery.

Based on your requirement for a cheap solution, an on-prem data warehouse might be more suitable if you already have the necessary infrastructure in place and can optimize costs over the long term. However, if your data and query volume are unpredictable or fluctuating, the pay-as-you-go model of BigQuery might be more cost-effective. Be sure to consider factors like upfront costs, ongoing management, and scalability in addition to the costs associated with data storage and query processing.