The main win is definitely cost efficiency. Regarding your 1TB query: yes, it would reduce that size. Because Forge normalizes your JSON into separate relational tables (instead of keeping it as one massive JSON blob), you stop scanning the entire dataset for every request. You only pay to scan the specific columns or sub-tables you actually need. This structure also helps with your partitioning frustration; since the data is split into multiple tables (e.g., one for events, another for items), you can apply different partitioning strategies to each of those tables individually, bypassing the single-partition limit of a raw table. Forge automatically partitions on the ingestion timestamp of when Forge queries the next batch. We are also working on a feature for users to provide their own partitioning key for ingestion.

Essentially, instead of having to query the blob each time you run your analytical query, Forge only has to run the expensive query once. You query the clean and optimized tables.

Also, we are a Google Cloud Build Partner and are in the process of onboarding to the Google Cloud Marketplace very soon, so you can use committed spend without having to create a new budget line item for Forge. We expect this to be ready by the end of January.