At the 2012 Turning Award conference in San Francisco, Prof William Kahan mentioned that he had a newer test suite available in 1993 that would have caught Intel's bug. Still, Intel did not run that.. Prof. Kahan was actively involved in its analysis and further testing. (I'm stating this just from memory).
In this article, we’ll give you an overview of the challenges of implementing column-wise storage for JSON and the techniques used to address these challenges.
Capella Columnar is an advanced real-time analytics database service from Couchbase, targeted for real-time data processing, offering SQL++ for processing JSON (semi-structured) data and more. This service enables data to be managed locally and streamed continuously from both relational and NoSQL databases, or simply process data on S3. The columns or fields of the source are directly mapped to a field in the JSON document at the destination automatically. This is really a zero-ETL operation. A key feature of this system is its ability to continuously stream data, making it immediately available for querying, thus ensuring near real-time data processing. The JSON analytics database engine is designed for MPP (Massively Parallel Processing), with a column store for JSON and a cost-based optimizer.
Given a schema, you can generate an infinite number of queries. The starting from the business question is a better choice. There are some interesting work done in this area, but still a long way to go. Here's an example: https://einstein.ai/static/images/pages/research/seq2sql/seq...