In this technologically advancing world, data is one of the most valuable assets in any technical field. Data exists in various formats and includes a lot of useful information. Regardless of the size or application of any project, a Database Management System (DBMS) is crucial for storing and managing the acquired or available data. Database Management Systems can be broadly classified into two categories – Relational (or SQL) and Non-Relational (or NoSQL).
Relational Database
A SQL (Structured Querry Language) database, often known as a relational database, is made up of several highly structured tables where each row represents a certain type of data item and each column designates a particular field of data. SQL creates, saves, updates, and retrieves data in relational databases. As a result, all relational database management systems (RDBMS), including MySQL, Oracle, and Sybase, use SQL as their core programming language. The data can be conveniently stored and accessed using simple SQL queries
In a relational database system, data is stored and arranged in tables. Tables are the basic database objects in RDBMS that are logically created to collect data in rows and columns format. Columns list the qualities of each item, whereas rows list the entities themselves. For instance, in a table of customer data, each row represents a record for a particular client, and each table column provides information specific to that customer, such as name and address.
In a relational database, the relation between different tables can be specified using some specific keys. A column that contains unique records for the entries in a table is known as the primary key for that table. The primary key can be used to uniquely identify an entry or a row in a relational table. This primary key when imported into another table that has some relation with the previous table is known as the foreign key. The relation between the two tables can be easily defined with the help of the foreign key. For example, consider a database containing the information of students. Two separate tables can be created: “Student” and “Grades”. Consider the table “Student” has attributes as ‘Roll_no’, ‘Name’, ‘Class’, ‘Address’ and the table “Grades” have the attributes ‘Roll_no’ and ‘Avg_Marks’. Here, for the table “Students”, each student has a unique roll number that can be used to uniquely identify that student. Hence, the attribute ‘Roll_no’ is the primary key for that table and a foreign key for the “Grades” table. And the relationship between the two tables, i.e. a student ‘has’ this particular grade can be established using the key ‘Roll_no’.
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Non-Relational Database
NoSQL databases (also refered as ”Not Only SQL”) differ from relational databases in the way they store data. Depending on the data model, there are many types of NoSQL databases like document, key-value, wide-column, and graph. They provide flexible schemas and scale well under heavy user loads and data loads. Unlike relational databases that employ SQL, non-relational databases use object-relational mapping (ORM) to make it easier to communicate with their data. The four most common forms of NoSQL databases are key-value pair, document-oriented, column-oriented, and graph databases. These forms can be used separately or in combination depending on the type and size of the data.
A column-oriented database stores and arranges the data as columns. It is used to store sparse data as it is more adjusted toward a data-organizing strategy that is column-oriented. If the data is a collection of fields and data values like a document, then document-oriented database is ideal for storing such data. They generally store data as JSON strings. A popular example of document-oriented non-relational DBMS is the MongoDB.
The key-value databases are similar to the Python dictionaries where each entry is stored as a key-value pair. Further, the graph databases are used for highly interconnected data that needs efficient implementations. It is considered as the most complex NoSQL database.
Choosing between SQL and NoSQL
Both, SQL and NoSQL databases have their own pros and cons and none of them can be considered as the superior one in all situations. Depending on the type of data, size of data, application, scalibility, and other factors, either of the two can be selected for efficient management and access of the data.
One aspect of choosing between SQL and NoSQL is the structure of the data. If the data is highly structured and the structure of the data does not change frequently, SQL is preferred for storing and managing such type of data as SQL databases offer a more rigid structure and schema. For unstructured or semi-structured data that cannot be represented in the relational format, NoSQL should be preferred as they allow greater flexibility when creating a schema and storing various data structures within the same database.
The SQL databases provide vertical scalability allowing the users to store more number of records in the existing schema. Whereas NoSQL databases are horizontally scalable, which allows the users to divide the data load over multiple servers. So, if the data is not varied in its type and does not require horizontal scaling, SQL databases might be the right choice.
Further, SQL databases support multi-record ACID (Atomicity, Consistency, Isolation, and Durability). Hence, SQL databases must be preferred over NoSQL databases in case of transaction-oriented systems such as accounting or financial applications. Although banks have built advanced SaaS and ERP applications, but still their fundamental backend uses SQL database and operations.
The design of SQL databases is very consistent. Complex searches against structured data, including ad hoc requests, are made simpler by SQL databases’ quick query processing and data combining across tables. On the other hand, NoSQL databases are generally not consistent like Cassandra. However, a few databases like MongoDB are consistent despite being non-relational databases.
Market research suggests more than 2000 enterprises have switched from SQL to NoSQL databases to support their mission-specific applications. Example: Marriott uses NoSQL stack for its reservation system, which generates an approximate revenue of $38 billion annually. Also, GE has established a NoSQL architecture for its Predix platform in assisting its operations via Industrial Internet.
Therefore, both the SQL and NoSQL databases find their use in different applications depending on the user’s requirements. For some applications, SQL can be a more suitable option while in other cases NoSQL would give better efficiency.
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