We have found BigQuery to work great on data sets larger than 100M rows, where the 'initialization time' doesn't really come into effect (or is negligible compared to the rest of the query).
However, on anything under that, the performance is quite slow and poor, which makes it (1) ill-suited to working in an interactive BI tool; and (2) inferior to other products, such as Redshift or even ElasticSearch where the data size is under 100M rows. Actually, we had an engineer at our organization that was evaluating a technology for doing queries on data sizes between 1M and 100M rows for an analytics product that has about 1000 users, and his feedback was that he could not believe how slow BigQuery was.
Without a defense of the BigQuery product, I was wondering if there were any plans on improving:
It's time spent on metadata/initiation, but actual execution time is very small. We have work in progress that will address this, but some of the changes are complicated and will take a while.
You can imagine that in its infancy, BigQuery could have central systems for managing jobs, metadata, etc. in a manner that performed very well for all N0 entities using the service. Once you get to N1 entities, however, it may be necessary to rearchitect some things to make them have as little latency as possible. For notification about new features--which is also where we would announce API improvements related to start-up latency--keep an eye on our release notes, which you can also subscribe to as an RSS feed.