OP wrote "I care most about data integrity, monitoring and maintainability, rather than performance;" - IMHO, a well designed relational database makes it easy to enforce data integrity, and with some carefully planed indexes, should have no performance problems.
I'm no expert when it comes to NoSql but I have been working with MongoDb for a couple of years (thought admittedly, not a very intensive work) and I've yet to encounter anything it can do faster than SQL Server - but that might be because of a lack of MongoDb knowledge on my part.
My point is, for a hard structured, relational data, I see no reason not to work with a relational database. Query speed optimizations can be made when needed, even at the cost of denormalized (flatten) data - yet still keeping the data integrity.
Sure maybe what OP wants in this specific case is SQL I wont argue that side. I also said at scale NoSQL is faster. SQL is faster when you are working with small datasets. However, once you have an application that is at scale, like say the company I'm working with right now that has 100 million records SQL is no longer performant. However, after being switch to NoSQL DynamoDB every single access pattern they have can be queried in under 40ms every single time.
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
OP wrote "I care most about data integrity, monitoring and maintainability, rather than performance;" - IMHO, a well designed relational database makes it easy to enforce data integrity, and with some carefully planed indexes, should have no performance problems.
I'm no expert when it comes to NoSql but I have been working with MongoDb for a couple of years (thought admittedly, not a very intensive work) and I've yet to encounter anything it can do faster than SQL Server - but that might be because of a lack of MongoDb knowledge on my part.
My point is, for a hard structured, relational data, I see no reason not to work with a relational database. Query speed optimizations can be made when needed, even at the cost of denormalized (flatten) data - yet still keeping the data integrity.
Sure maybe what OP wants in this specific case is SQL I wont argue that side. I also said at scale NoSQL is faster. SQL is faster when you are working with small datasets. However, once you have an application that is at scale, like say the company I'm working with right now that has 100 million records SQL is no longer performant. However, after being switch to NoSQL DynamoDB every single access pattern they have can be queried in under 40ms every single time.