Most SQL systems were designed for transactional data processing. Many of these OLTP systems work with data one tuple at a time and are unsuitable for processing large amounts of data especially when analytical queries are involved. Analytic RDBMS products - mostly those that have a columnar architecture - generally do a much better job but are still plagued by the limitations of SQL when it comes to processing this kind of data. Rigid database schemas get in the way of application development and ongoing schema maintenance becomes a burden. Due to schema rigidity, data needs to be denormalized and stored in different tables. This introduces another complexity cost while performance also suffers.
To come around these limitations IT engineers frequently combine document databases or other NoSQL solutions with RDBMS products in order to store and process denormalized data. Unfortunately most of these document database systems suffer from the same issue that OLTP systems have when it comes to analytical type workloads. Some don't support any analytical queries at all and the sole purpose of using a NoSQL solution is to be able to easily store semi-structured data.
The Raijin Database engine is nowhere near complete. There is still an extreme amount of work to be done on features currently missing that we intend to implement, including but not limited to the following:
On the other hand there are features found in other SQL solutions that are not in-line with the design goals of Raijin and will probably not be implemented in the near future:
The schemaless database engine built for today's data challenges