It's hard to deliver "one fast, secure SQL query on all your data". If you look around you'll find lots of "SQL on Hadoop" implementations which are unaware of data that's not on Hadoop. And then you'll see other solutions that combine the results of two different SQL queries, written in two different dialects, and run mostly independently on two different platforms. That means that while they may work, the person writing the SQL is effectively responsible for optimizing that joint query and implementing the different parts in those two different dialects. Even if you get the different parts right, the end result is more I/O, more data movement and lower performance.
Big Data SQL is different in several ways. (Start with this blog to get the details). From the viewpoint of the user you get one single query, in a modern, fully functional dialect of SQL. The data can be located in multiple places (Hadoop, NoSQL databases and Oracle Database) and software, not a human, does all the planning and optimization to accelerate performance.
Under the covers, one of the key things it tries to do is minimize I/O and minimize data movement so that queries run faster. It does that by trying to push down as much processing as possible to where the data is located. Big Data SQL 3.0 completes that task: now all the processing that can be pushed down, is pushed down. I'll give an example in the next post.
What this means is cross-platform queries that are as easy to write, and as highly performant, as a query written just for one platform. Big Data SQL 3.0 further improves the "fast" part of "one fast, secure SQL query on all your data". We'd encourage you to test it against anything else out there, whether it's a true cross-platform solution or even something that just runs on one platform.