Introduction

Most database workers are familiar with SQL since it has been the primary method of accessing relational databases for many years. As unstructured data, storage and processing capacity, and analytics have evolved over time, new database systems have been accessible that are better suited to various new use cases. Commonly referred to as NoSQL databases, these databases are. Knowing whether to utilize a SQL database or a NoSQL database for the application is the first decision that each database administrator must make. What makes the two different?

SQL database

Since 1970s, relational database management systems (RDBMS) have been using the programming language known as SQL, or Structured Query Language, to manage data. They are the most widely used and aid in the processing of structured data, which arranges data pieces and uniformizes their connections to one another and to different attributes. These relational databases can manage enormous volumes of data and intricate SQL queries while offering quick data storage and recovery.

NoSQL database

These databases, which are often referred to as non-relational databases, enable the use of a dynamic schema to store and retrieve unstructured data. NoSQL is frequently used because of its adaptability in building custom structures; these structures can be documents, graphs, columns, or even Key Values structured as data structures. The requirements put out for the creation of contemporary applications led to the development of these databases. Although NoSQL databases don't always allow SQL commands, this doesn't imply that the systems don't employ SQL. “NoSQL” is sometimes defined as “not only SQL.”

Difference between SQL and NoSQL:

There are a lot of differences between SQL and NoSQL, practical differences between SQL and NoSQL are :

1. Language:

Structured Query Language is used by SQL databases to manage and manipulate data. It is incredibly strong, adaptable, and frequently used on the one hand, yet extremely constrictive on the other. The data structure in SQL is examined using specified schemas, which must be followed. Any changes to the data structure would be disruptive and exceedingly challenging.

The data can be organised as a document-oriented, graph-based, or columnoriented database in a NoSQL database, which has a dynamic schema for unstructured data. There are no limits, much like in SQL; you can create documents without knowing their structure beforehand, and each document's structure, along with the number of fields and database's syntax, can differ.

2. Scalability:

SQL databases are vertically scalable in the vast majority of applications. It implies that you can expand CPU, RAM, or SSD to increase the demand on a single server. The most traffic can be handled by NoSQL databases, on the other hand, by simply adding more servers to the database because they are horizontally scalable. Due to its capacity to grow, NoSQL is preferred for big datasets that are continually changing.

3. Structure:

Always representing relational, tabular data with rules for consistency and integrity are SQL database schemata. They contain tables with rows of entries and columns of characteristics, and the keys have limited logical associations. Although they are not required to follow this pattern, NoSQL databases often fall into one of four broad categories:

• By transposing row-oriented RDBMSs, column-oriented databases enable the effective storing of high-dimensional data and individual records with various properties.
• Dictionary-style key-value storage allow users to access a variety of objects with a single key.
• Semi-structured data, which consists of items that are totally distinct from one another and each include all of their own pertinent information, is stored in document stores.
• Graph databases enable quick exploration of highly connected data sets by introducing the concept of relationships (direct links between objects) to texts.

4. Properties

The abbreviation ACID stands for the four properties that RDBMS, which employs SQL, must possess. These provide successful transaction processing and a high level of dependability for the SQL database:

• Atomicity: Even in the event of a system failure, all transactions must complete successfully or utterly fail.
• Consistency: At every stage, the database must adhere to standards that validate and prevent corruption.
• Isolation: Parallel transactions can't interfere with one another.
• Durability: Since transactions are final, they cannot be "rolled back" in the event of a system failure.

Although some NoSQL databases, including IBM's DB2, MongoDB, AWS's DynamoDB, and Apache's CouchDB, can also integrate and follow ACID standards, NoSQL adheres to the CAP theory while SQL demands ACID qualities.
The CAP theorem states that distributed data systems permit a trade-off that can only ensure two of the following three features (which form the acronym CAP) at any particular time:

• Consistency: Each request is always met with either the most recent response or an error. Strongly consistent systems include MongoDB, but eventual consistent systems like Cassandra give eventual consistency.
• Availability: All requests result in non-errors.
• Partition tolerance: The system is not interrupted by delays or losses between nodes.

Examples of SQL databases

Db2, MySQL, PostgreSQL, YugabyteDB, CockroachDB, Oracle Database, Microsoft SQL Server, Azure SQL Database

Examples of NoSQL databases

Redis, FaunaDB, CouchDB, MongoDB, Cassandra, Elasticsearch, BigTable, Neo4j, HBase

When to use SQL vs NoSQL
NoSQL is typically recommended for things like:

• Graph or hierarchical data;
• Large, frequently changing data sets;
• Businesses expanding quickly without data schemata.

This could apply to social networks, online content management, streaming analytics, or mobile applications in terms of use cases.

SQL is more suitable when the data is:

• Small
• Conceptually modelled as tabular
• In systems where consistency is crucial.

Consider sales databases, transactional systems like e-commerce payment processing, or small business accounting systems. Because RDBMSs are more resilient to faults and have stronger support, SQL is also more appropriate in uncertainty.