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NoSQL - catch-all term for databases that stray from the relational database model (graph DBs, document DBs, key-value stores, etc.)SQL - from Structured Query Language, a language used to query relational databases (although NoSQL databases often support some very limited subset of SQL, to ease the transition a bit).MySQL - a RDBMS (Relational Database Management System) - a program that knows what to do with relational data, and can accept SQL, build a program that does what you said, and return the results.TL;DR: The first is a term that refers to "non-relational" databases, the second is a language, the third is a program.

Is this has to do because they are NoSQL and are eventual consistent?It has to do with the fact that they are Document-Oriented DBs. As such, they only provide consistency on a single document. MongoDB and Cassandra have completely different notions of consistency, so answering this in more detail basically requires a lesson in both.But the other thing to keep in mind here is that both DBs are designed to be scaled across multiple servers. If you support transactions across documents, that also means that they have to work across servers and that is a non-trivial task. So these DBs simply don't do it.

“NoSQL” isn’t a new age - it’s actually a very old idea in new packaging for the most part. And there isn’t a well-defined “NoSQL” standard that could be published by a standards committee, published in textbooks, or produced into Youtube instructional videos.That said, mostly what is being referred to by NoSQL is:Document-oriented databases such as MongoDB and Cassandra, the two most popular NoSQL DBs. These are great for their use-cases, which is mainly loads, updates, or lookups of structured “files” of stuff that is organized around a small number of keys, but they either don’t do transactions or they do them in a deferred manner (Quorum) that is hard or impossible to use for many use-cases that require full ACID.Graph databases are the modern take on Network model databases - which is actually among the oldest formal data models - and are awesome for their use-cases, but even their biggest fans won’t assert that they could replace relational databases for general business data management.Sometimes people include large cache managers like Redis in NoSQL databases, although this is both unfair to “real” data managers and Redis itself, which is awesomely useful for high-performance worlds like highly active websites. But Redis doesn’t support “queries” and certainly doesn’t do persistence or crash recovery (although you can use replication cleverness with multiple hosts and get a reasonably reliable Redis dataworld).Probably the biggest thing that keeps relational databases going is the basic concept of the Relational join as an operation computed using user-visible and managed data in the database by the database engine itself. This is in opposition to the idea of a Hard link relationship between two pieces of data, which is how just about everyone other than relational databases represents relationships between data, either explicitly as in graph DBs or network/hierarchical DBs, or implicitly by bundling related data into physical groups as is done in document databases.Hard links are fast, but computed joins or links are flexible.So, they both have their use cases, and will for the forseeable future.