I don't think it's always that easy. I work on some large databases (1TB+) where even queries utilizing indexed columns still take prohibitively long to run. Sure we could look into partitioning (and we likely might) but then more work is progressively falling on engineering to keep the system running for the standards of analysts but not necessarily the product.

BigQuery in these cases has been very useful. Everything I throw at it returns in seconds. I can also do more text comparison queries that on a standard RDBMS would have required a full text index (which would probably be huge). But with BigQuery even those queries take seconds and I don't have to take production resources to view it.

Sounds like you need a better index :-)

If you need fulltext search, I've enjoyed using Sphinx and Lucene in the past. Is the column you want to do fulltext on 1 TB? 1 TB of RAM in 128 gig DIMMs is <$10k these days, so might as well get at least that much if you're running anything like the bills these guys are talking about.

Importantly it depends on how often these things are run and who has to maintain a server.

The example in the article is 40k/day for querying on 14 GB of data, every second. You can do that on a ~100/month machine or a somewhat modern laptop.

Our entire, hundreds of millions of dollars business with hundreds of millions of orders of historical data can be "explored" by a data scientist on an average macbook.

If your data scientists can't do data science, then that's pretty bad

It depends on the business unit. Some don't have the ability to run a technology group to run a $10K dedicated server.

Query bills of $40k a day will pay for a lot of staff, or a lot of outside expertise to administer it for you.

I feel like for 1.4 million USD a year I could solve that problem for them.

But they have the budget to run $10K of queries a day?