Backend Developer

Approach :

1. For every row in the given 2D array do the following : 
Initialize the start index as 0 and end index as N-1.
Iterate loop till start index is less than ending index, swap the value at these indexes and update the index as : 
swap(arr[i][start], arr[i][end])
start++;
end--;

2. Do the above operation for all the rows in the 2D array.

1. Type – 
SQL databases are primarily called as Relational Databases (RDBMS); whereas NoSQL database are primarily called as non-relational or distributed database. 

2. Language – 
SQL databases defines and manipulates data based structured query language (SQL). Seeing from a side this language is extremely powerful. SQL is one of the most versatile and widely-used options available which makes it a safe choice especially for great complex queries. But from other side it can be restrictive. SQL requires you to use predefined schemas to determine the structure of your data before you work with it. Also all of your data must follow the same structure. 
A NoSQL database has dynamic schema for unstructured data. Data is stored in many ways which means it can be document-oriented, column-oriented, graph-based or organized as a KeyValue store. This flexibility means that documents can be created without having defined structure first. Also each document can have its own unique structure. 

3. The Scalability – 
In almost all situations SQL databases are vertically scalable. This means that you can increase the load on a single server by increasing things like RAM, CPU or SSD. 
But on the other hand NoSQL databases are horizontally scalable. This means that you handle more traffic by sharding, or adding more servers in your NoSQL database. It is similar to adding more floors to the same building versus adding more buildings to the neighborhood. Thus NoSQL can ultimately become larger and more powerful, making these databases the preferred choice for large or ever-changing data sets. 

4. The Structure – 
SQL databases are table-based on the other hand NoSQL databases are either key-value pairs, document-based, graph databases or wide-column stores. This makes relational SQL databases a better option for applications that require multi-row transactions such as an accounting system or for legacy systems that were built for a relational structure. 

5. Property followed – 
SQL databases follow ACID properties (Atomicity, Consistency, Isolation and Durability) whereas the NoSQL database follows the Brewers CAP theorem (Consistency, Availability and Partition tolerance). 

6. Support – 
Great support is available for all SQL database from their vendors. Also a lot of independent consultations are there who can help you with SQL database for a very large scale deployments but for some NoSQL database you still have to rely on community support and only limited outside experts are available for setting up and deploying your large scale NoSQL deployments. 
Some examples of SQL databases include PostgreSQL, MySQL, Oracle and Microsoft SQL Server. NoSQL database examples include Redis, RavenDB Cassandra, MongoDB, BigTable, HBase, Neo4j and CouchDB.

An aggregate function is an SQL function that reads data from multiple rows and displays a single value. Some examples of aggregate functions are COUNT, SUM, MIN, MAX, and AVG. They are often used with a GROUP BY clause but can be used by themselves.