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SQL vs. NoSQL: Definition, Databases, and Their Differences

Last updated: Dec 20, 2023

SQL vs. NoSQL: Definition, Databases, and Their Differences
Cover image: Illustration of SQL vs NoSQL.

What is SQL?

Before exploring the differences between SQL vs NoSQL, let's first learn the definitions of what is SQL and NoSQL

SQL stands for Structured Query Language, a programming language that plays a crucial role in storing, managing, accessing, and modifying relational-based data.

In the realm of computer technology and networks, SQL is a programming language that has ANSI (American National Standard Institute) standardization and has been around since 1970. 

When it was first launched, this query language was still called SEQUEL (Structured English Query Language). However, shortly afterward, the name SEQUEL was changed to SQL for easier pronunciation.

SQL has been around for decades and remains one of the most frequently used programming language options to this day.

Many databases follow the basic SQL guidelines, including PostgreSQL, MySQL, Oracle, and Microsoft SQL Server.

SQL is a database paradigm that focuses on data accuracy and structure by establishing a strict schema. 

This means that the structure and relationships between tables must be defined in advance. In short, in the SQL system, data is stored in related tables, and queries are performed using SQL statements. 

This is why SQL is considered a programming language suitable for handling complex data with intricate relationships.

What is NoSQL?

NoSQL is a database paradigm designed to meet the needs of complex performance, especially in managing large amounts of data with an uncertain schema.

Not Only SQL or NoSQL is a non-relational database management system (DBMS) that is well-suited for handling big data and is often subject to frequent changes over time.

Due to these advantages, many large companies like Google and Facebook have been proven to use NoSQL in their database management processes.

The reason for this is that NoSQL can proactively support web applications and all changes in the data that occur in real-time.

Some examples of NoSQL databases are MongoDB, Cassandra, and Redis, which are considered more flexible for storing and accessing data as they do not require complex queries.

Differences Between SQL dan NoSQL?

After understanding the basic definitions of what is NoSQL and SQL, let's delve deeper into their differences. Here are 7 differences between SQL and NoSQL along with comprehensive explanations:

1. The Structure Used

The first difference between SQL and NoSQL can be observed in their structures. One characteristic of NoSQL databases is having a much more flexible structure. In NoSQL, managed data can even be stored in graph databases, documents, or key-value stores.

On the other hand, SQL follows a relational database structure. This means that data is stored in rows, columns, and tables in a structured manner. In fact, the table structure must be defined in advance before inputting data.

2. Programming Language

Since NoSQL is a flexible option, it can use various languages depending on the type of database. 

For example, the MongoDB database uses the JSON format and provides APIs based on JavaScript, Python, and others.

On the contrary, SQL uses the standard query language to manipulate data in relational databases. 

This language has become an industry standard and is widely used in various relational database systems. SQL queries include statements such as SELECT, INSERT, UPDATE, and DELETE to perform operations on data.

3. Usage in Web Apps

Another difference between SQL vs NoSQL lies in their usage. In broad terms, NoSQL is often used in web apps that require high loading speed, such as on game web pages, IoT, and some social media platforms.

On the other hand, SQL is a more suitable option for business, communication, or financial applications that require high data integrity and tend to be complex.

 4. Budget and Costs

Costs are a critical factor in database system management. The difference between SQL vs NoSQL in terms of costs can significantly impact the development and operational decisions of an application.

From a financial perspective, NoSQL is often considered a more economical choice because it provides many open-source platforms. 

One advantage of implementing a NoSQL database is that it can be downloaded and used without significant licensing costs. 

Additionally, maintenance costs for NoSQL are quite affordable because they do not require administrators with high-level skills.

In contrast to NoSQL, the budget required for SQL tends to be higher because users have to incur additional costs for software licenses, making it less suitable for small and medium-sized enterprises.

5. Required Data Schema

One fundamental difference between SQL vs NoSQL can also be seen in how they handle data schemas.

In SQL, the data schema is a crucial aspect and must be determined before data is inserted into the database. This means that every table structure, the relationship between tables, and data type must be defined strictly from the beginning. 

This approach can enhance high accuracy in data structure, but it can sometimes be a constraint, especially when users need to make changes to the established schema.

On the other hand, NoSQL offers a much more flexible approach when it comes to data schema. NoSQL databases do not require a fixed schema as predetermined. This way, the development team can store data with different structures in one collection or table.

6. Security

In addition to covering data structure and flexibility, another difference between SQL and NoSQL lies in the level of security when in use.

One characteristic of SQL is its high level of security, supported by various integrated security mechanisms. 

SQL provides features such as authentication, authorization, and encryption to protect data from unauthorized access and minimize the risk of data theft.

On the other hand, security in NoSQL is often considered less robust. NoSQL is designed to handle large data volumes, so some implementations may sacrifice certain aspects of security.

Nevertheless, some NoSQL databases now provide encryption activation options to control access and enhance data security.

7. Support and Community

The difference between SQL vs NoSQL can also be seen in the support and communities that use them.

SQL, being one of the more well-known query languages, is undoubtedly supported by a large community. 

It also has a stable codebase and is equipped with industry standardization that proves its quality. 

Due to its popularity, users can easily obtain a wealth of information, tutorials, and documentation related to SQL.

Relational SQL is generally developed by large vendors or exclusively owned by a company. In contrast, NoSQL has a shared community that ensures sustainability and organic development.

8. Consistency

NoSQL is a database that uses the BASE (Basically Available, Soft State, and Eventually consistent) consistency model. 

This means that NoSQL prioritizes availability and data partitioning in the database system. Here is a further explanation of the BASE concept:

  • Basically available: Read and write operations are always available on all nodes.
  • Soft state (S): Without a consistency guarantee, users can only predict the status with a certain probability.
  • Eventually consistent (E): If the system functions, the data will eventually reach a consistent state over time.

With this approach, NoSQL can ensure more flexibility for users in partitioned conditions by allowing some parts of the system to continue functioning even in the event of failure or network partition.

Meanwhile, SQL consistency uses the ACID model (Atomicity, Consistency, Isolation, Durability) that emphasizes a high level of consistency. Further details of the ACID model are as follows:

  • Atomicity: All transactions will succeed or fail as a single unit.
  • Consistency: Each stage must consistently follow rules validated by the system.
  • Isolation: To prevent concurrently running transactions from affecting each other. Each transaction must be sequential, even if executed in parallel.
  • Durability: System failure does not impact the success of transactions.

This means that every transaction must adhere to rules under specific conditions before being considered successful. 

ACID consistency ensures that each transaction originates from an atomic entity. In other words, if a failure or error is detected.

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