SQL vs NOSQL — What’s the best Database paradigm for your next project

A little bit of Introduction

SQL (Structured Query Language) databases have served as the primary data storage mechanism for more than four decades. The popularity of databases skyrocketed in the late 1990s, thanks to the proliferation of web-based applications and open-source alternatives such as MySQL, PostgreSQL, and SQLite.

NoSQL databases, which have been around since the 1960s, have recently gained popularity thanks to popular options such as MongoDB, CouchDB, Redis, and Apache Cassandra. At the same time, many tutorials exist that explain how to use a particular flavor of SQL or NoSQL, few explanations why you should choose one over the other. I hope to fill in the blanks. This article will go over some of the most important distinctions. Typical scenarios will be examined in a later follow-up article, and the most appropriate option will be determined. Most of the examples apply to the widely used MySQL SQL and MongoDB NoSQL database systems. There will be minor differences in features and syntax between other SQL/NoSQL databases, but they will be similar in structure.

How these databases handle simple set of data

SQL :

NoSQL:

So what’s the difference between the two?

1. Type

SQL databases are referred to as Relational Database Management Systems (RDBMS), while NoSQL databases are referred to as non-relational or distributed databases.

2. Language

SQL databases define and manipulate structured query language based on data (SQL). This language, when viewed from a distance, is compelling. SQL is one of the most versatile and widely-used options available, making it a safe bet, particularly for complex queries that require excellent performance. However, from another perspective, it can be limiting. Before working with SQL, you must determine your data structure by referring to predefined schemas available online. Additionally, the Developer must organize all of your data in the same way. A change in the plan could be challenging and disruptive to your entire system because it could require extensive up-front preparation.

Unstructured data is stored in a NoSQL database with a dynamic schema. NoSQL can store data in a variety of formats, including document-oriented, column-oriented, graph-based, and as a Key-Value store, among others. Because of its flexibility, NoSQL can create documents without a predefined structure first. Additionally, each document can have a distinct design. There are slight differences in syntax from one database to the next, and you can add fields as you go.

3. The Scalability

SQL databases are capable of being scaled vertically in almost all situations. In other words, you can increase the load on a single server by increasing the amount of RAM, CPU, or SSD it has. On the other hand, NoSQL databases are capable of being scaled horizontally. This means that you can handle more traffic by sharing your NoSQL database or adding more servers to your existing NoSQL database. It is analogous to adding more floors to the same building and adding more facilities to the surrounding area. In this way, NoSQL databases can grow in size and power over time, making them the preferred choice for large or constantly changing data sets.

4. The Structure

SQL databases are organized into tables. On the other hand, NoSQL databases are classified as key-value pairs, document-based, graph databases, or wide-column stores, among other things. SQL databases built for a relational structure are a better choice for applications that require multi-row transactions such as an accounting system or for legacy systems designed for a relational structure.

5. Property followed

Databases that use SQL adhere to the ACID properties (atomicity, consistency, isolation, and durability), whereas databases that use NoSQL adhere to the Brewers CAP theorem (consistency, isolation, and durability) (Consistency, Availability and Partition tolerance).

6. Support

All SQL databases have excellent support available from the companies that make them. Additionally, many independent consultations can assist you with SQL databases for large-scale deployments on a contract basis. However, in the case of some NoSQL databases, you will still have to rely on community support. Only a few outside experts are available to assist you in setting up and deploying your large scale NoSQL implementations.

SQL databases include PostgreSQL, MySQL, Oracle, and Microsoft SQL Server, to name a few examples. Examples of NoSQL databases include Redis, RavenDB, Cassandra, MongoDB, BigTable, HBase, Neo4j, and CouchDB, to name a few.

So, what’s best for your next project?

As a general rule, NoSQL databases perform exceptionally well when dealing with large amounts of real-time data, content management, and Internet of Things applications. As a result, SQL databases are preferred when the Server must regularly perform complex queries, such as online transaction processing and banking — asking whether an RDBMS or a NoSQL database is better without considering the project. It’s about as helpful as asking if you should use a fork or a spoon before ordering a meal at a restaurant. Each project has its own set of requirements. Even though SQL databases are frequently the first choice for developers, there is no objectively “better” database philosophy in general. Before making a final decision, determine the project’s scope at hand.