SQL Databases Vs NoSQL Databases

When people are comparing SQL and noSQL, and concluding the upsides and downsides of each one, what I never hear anyone talking about is the speed.
Isn't performing SQL queries generally faster than performing noSQL queries?
I mean, for me this would be a really obvious conclusion, because you should always be able to find something faster if you know the structure of your database than if you don't.
But people never seem to mention this, So I have decided to go with this writeup it would help everyone to select best database in the war between SQL vs NoSQL Databases.

SQL Databases	NoSQL Databases
SQL databases are primarily called as Relational Databases (RDBMS	NoSQL database are primarily called as non-relational or distributed database.
SQL databases are table based databases,	NoSQL databases are document based, key-value pairs, graph databases or wide-column stores.
SQL databases represent data in form of tables which consists of n number of rows of data	NoSQL databases are the collection of key-value pair, documents, graph databases or wide-column stores which do not have standard schema definitions which it needs to adhere to.
SQL databases have predefined schema	NoSQL databases have dynamic schema for unstructured data.
SQL databases are vertically scalable; they are scaled by increasing the horse-power of the hardware.	NoSQL databases are horizontally scalable; these databases are scaled by increasing the databases servers in the pool of resources to reduce the load.
SQL databases use SQL (structured query language) for defining and manipulating the data, which is very powerful.	NoSQL database, queries are focused on collection of documents. Sometimes it is also called as UnQL (Unstructured Query Language). The syntax of using UnQL varies from database to database.
SQL database examples: MySql, Oracle, Sqlite, Postgres and MS-SQL.	NoSQL database examples: MongoDB, BigTable, Redis, RavenDb, Cassandra, Hbase, Neo4j and CouchDb
For complex queries: SQL databases are good fit for the complex query intensive environment	NoSQL databases are not good fit for complex queries. On a high-level, NoSQL don’t have standard interfaces to perform complex queries, and the queries themselves in NoSQL are not as powerful as SQL query language.
For the type of data to be stored: SQL databases are not best fit for hierarchical data storage	NoSQL database fits better for the hierarchical data storage as it follows the key-value pair way of storing data similar to JSON data. NoSQL database are highly preferred for large data set (i.e for big data). Hbase is an example for this purpose.
For scalability: In most typical situations, SQL databases are vertically scalable. You can manage increasing load by increasing the CPU, RAM, SSD, etc, on a single server.	NoSQL databases are horizontally scalable. You can just add few more servers easily in your NoSQL database infrastructure to handle the large traffic.
For high transactional based application: SQL databases are best fit for heavy duty transactional type applications, as it is more stable and promises the atomicity as well as integrity of the data.	You can use NoSQL for transactions purpose; it is still not comparable and sable enough in high load and for complex transactional applications.
For support: Excellent supports are available for all SQL database from their vendors. There are also lot of independent consultations who can help you with SQL database for a very large scale deployments.	For some NoSQL database you still have to rely on community support, and only limited outside experts are available for you to setup and deploy your large scale NoSQL deployments.
For properties: SQL databases emphasizes on ACID properties ( Atomicity, Consistency, Isolation and Durability)	NoSQL database follows the Brewers CAP theorem ( Consistency, Availability and Partition tolerance )
For DB types: On a high-level, we can classify SQL databases as either open-source or close-sourced from commercial vendors	NoSQL databases can be classified on the basis of way of storing data as graph databases, key-value store databases, document store databases, column store database and XML databases.