There has indeed been a ton of research on SQL databases. But still, Stonebraker, one of their pioneers, said that they should all be thrown out:
"We conclude that the current RDBMS code lines, while
attempting to be a “one size fits all” solution, in fact, excel at nothing. Hence, they are 25 year old legacy code lines that should be retired in favor of a collection of “from scratch” specialized engines. The DBMS vendors (and the research community) should start with a clean sheet of paper and design systems for tomorrow’s requirements, not continue to push code lines and architectures designed for yesterday’s needs." -- http://nms.csail.mit.edu/~stavros/pubs/hstore.pdf
I'm sure a lot of their intellectual work is indeed something I could learn from. But SQL databases are an artifact of a particular moment in technological and cultural history. The thing I really want to learn about isn't the residue of their thoughts as interpreted through 30 years of old code, it's their original insights and how to transfer those to today's world.
Hadoop is a fine example of Stonebraker's point. The original sweet spot of relational databases was easy queries across volumes of data too large to fit in RAM. But Google realized early on that they could do orders of magnitude better with a different approach.
I agree that these new approaches haven't been fully proven in the field, but I'm certainly glad that people are trying.
As a side note, I think the right way to solve the pseudo-English query language problem is by making the query language actual English. If you have a programmer and an analyst or other business person sit next to one another, you get much better results than either one working alone.
Stonebraker's research and commercial ventures the last several years have been focused on building specialized variants of existing database systems. Vertica (C-Store), VoltDB (H-Store), Streambase (Aurora), and SciDB are all specialized DBMS systems designed to overcome the one size fits all nature of things.
Regardless, there's always going to be a balance between specialized systems and platforms, but my point is that we should be willing to trust the platforms that have proven themselves, avoid reinventing the wheel (poorly), and not be too quick to throw them out in favor of the new shiny.
I agree that programmer/analyst working together is a terrific pair, but the beauty of software is that we live in a world where we can have 1 programmer write a platform on which 10 programmers build 10 systems that 1000 users use to get their jobs done and make society that much more efficient.
Oh, I trust the current stable platforms to be the current stable platforms. My beef isn't with people who use them. It's with people who don't know how to do anything else, which was a plague on our industry for at least a decade. At least the people who get burnt on the latest fad will make new and interesting mistakes.
I agree that when we can find ways to let users serve themselves, that's best for everybody. I just don't think universal pseudo-English query languages are the way to do that, because the hard part isn't learning a little syntax, it's learning what the syntax represents in terms of machine operations.
Once the programmer and the analyst have found something stable enough to automate, by all means automate it. Reports, report builders, DSLs, data dumps, specialized analytic tools: all great in the right conditions. But people have been trying to build pseudo-English PHB-friendly tools for decades with near zero uptake from that audience. I think there's a reason for that.