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#2 Differences between SQL and NoSQL

In the world of database management systems, two prominent players stand out: SQL (Structured Query Language) and NoSQL (Not Only SQL). While both serve the purpose of storing and managing data, they differ significantly in their approach, structure, and use cases. Let’s delve into the key disparities between these two types of databases to better understand their strengths and weaknesses.

Data Structure

SQL: Relational databases, synonymous with SQL databases, organize data into structured tables comprising rows and columns. They enforce a rigid schema, necessitating the definition of data structure before insertion.

NoSQL: Contrastingly, NoSQL databases offer flexibility with a schema-less approach. They can handle structured, semi-structured, or unstructured data, supporting diverse formats such as key-value pairs, documents, graphs, or wide-column stores.


SQL: SQL databases traditionally scale vertically, requiring upgrades to single servers to accommodate increased loads. This scalability approach can be costly and has inherent limitations.

NoSQL: NoSQL databases are designed for horizontal scalability, allowing for the addition of more servers to distribute workload. This makes them ideal for managing extensive data volumes and high traffic.

Query Language

SQL: SQL databases utilize a standardized query language (SQL) for defining, querying, and manipulating data. SQL offers robust capabilities for complex queries, joins, and transactions.

NoSQL: NoSQL databases often feature proprietary query languages or APIs tailored to their specific data models. While powerful, their query capabilities may not match those of SQL, particularly for intricate queries involving multiple collections or documents.


SQL: SQL databases adhere to ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring data consistency and integrity. Transactions in SQL databases follow strict rules to maintain data correctness.

NoSQL: NoSQL databases often prioritize performance and scalability over strict adherence to ACID properties. Some opt for eventual consistency, guaranteeing data consistency over time rather than immediately after an operation.

Data Model

SQL: Relational databases adopt a tabular data model, organising data into tables with predefined schemas and establishing relationships using foreign keys.

NoSQL: NoSQL databases support various data models, including key-value, document, column-family, and graph. Each model is optimised for specific data types and use cases.

Database Examples

SQL Server
Azure SQL Database
Cloud SQL
Azure Cosmos DB
Cloud Firestore


System software called SQL databases facilitates the management, analysis, capture, and querying of structured data in a relational manner.A type of software called a no-SQL database enables the maintenance and retrieval of structured, unstructured, and polymorphic data for various purposes.
a language for storing, deleting, updating, inserting, and retrieving data from databases.a application to access databases and manage their scalability.
Structured Query Languages are supported by SQL databases.There is no declarative query language in nonSQL. Each database provider has its own language.
Supports table based data type.supports key-value pair-based, graph databases, and document-oriented databases.
The storing of data hierarchically is not supported by SQL.The best database for storing hierarchical data is NoSQL.
Predefined schemas are supported by SQL, limiting the storing of data to just structured types.Dynamic schemas are supported by Nosql for storing various types of data.
The ACID (atomicity, consistency, isolation, and durability) qualities are supported by SQL.The CAP (consistency, availability, partition tolerance) principles apply to non-SQL.
SQL databases do not adhere to data dissemination because they can only be run on a single system.The design of NoSQL databases incorporates data distribution aspects like repetition and partition.