SQL vs. NoSQL Today: Databases, Differences & When To Use Which

SQL and NoSQL are two database technologies widely adopted by many organizations for different use cases. Both technologies share the common goal of efficiently processing and managing data. Still, there are some significant differences. 

This article compares SQL and NoSQL, exploring their key differences in terms of language, structure, scalability, properties and support. We’ll also discuss examples, pros and cons and the most suitable application areas for each database type.

What is SQL?

SQL, short for Structured Query Language, is a widely used database query language developed by IBM in 1970. Originally known as SEQUEL, it became publicly available in 1979. That’s probably why, even today, some people say SQL as “sequel” while the generally accepted pronunciation is “ess-queue-elle” for the 3 letters.

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SQL allows users to perform CRUD (Create, Read, Update, and Delete) operations on relational database management systems (RDBMS) based on a structured and tabular format. Users can write SQL queries using keywords and syntax defined by the SQL standard. 

(Read our full explainer on database management systems.)

Examples of SQL databases

Popular databases that use SQL for data processing are:

• MySQL is a widely-used open-source RDBMS. It is cross-platform and scalable and integrates well with different programming languages and tools.
• PostgreSQL is an open-source RDBMS with advanced features like support for complex data types.
• MS SQL Server isa n RDBMS developed by Microsoft. It is used by businesses for managing and analyzing structured data.
• Oracle RDBMS is a proprietary RDBMS developed by Oracle. It offers enterprise-grade features like security features, backup and recovery mechanisms.
• Amazon RDS is the SQL Server that operates in the AWS cloud environment and is fully managed by AWS.

What is NoSQL?

Short for Not only SQL, NoSQL is a relatively new technology, initially used in 1998 by Carlo Strozzi. It is a language used to query non-relational databases such as:

• Key-value stores
• Document stores
• Graph databases

NoSQL provides a flexible data model that can be used to handle unstructured, semi-structured and rapidly changing data.

Examples of NoSQL databases

Popular databases that use NoSQL for data processing are:

• MongoDB is a popular document-oriented NoSQL database that supports JSON-based dynamic schemas.
• Redis is a popular open-source, in-memory key-value data store. 
• Apache Cassandra is a distributed NoSQL DBMS designed for handling large amounts of data.
• HBase is an open-source column store built on top of Apache Hadoop. 
• DynamoDB is a fully managed NoSQL database service provided by Amazon AWS. It has very low latency for read and write operations.

Key differences between SQL & NoSQL

SQL and NoSQL differ mainly in terms of their languages, supported data types, scalability, community support and key properties. 

Language

SQL is a declarative language. It means you have to specify the data you want to retrieve or modify, and the language implementation will handle the rest.

This language is generally easy to understand.  SQL uses the SELECT, INSERT, UPDATE and DELETE keywords to retrieve or modify data in tables while using CREATE and DROP to interact with the database. Additionally, users can:

• Join tables to perform complex operations based on common columns.
• Define relationships between tables.

On the other hand, NoSQL does not have a standardized query language like SQL. Each NoSQL database has its own specific query language, depending on its data model and design. NoSQL leverages data formats like JSON, XML and YML to support unstructured data.

NoSQL is a more flexible language than SQL as it supports dynamic schemas. Similar to SQL, NoSQL provides capabilities for aggregating and processing data.

Data structures

As the name implies, SQL allows performing query operations on relational or tabular data and returns the data in a structured data model consisting of rows and columns. Its strict predefined schema requires users to structure and organize data before performing the query operations. The relationships between tables are established using keys. 

In contrast, NoSQL databases do not rely on a specific format and instead offer support for various formats such as:

• Key-value stores store data as a collection of key-value pairs.
• Document stores utilize JSON or XML for storing data.
• Graph databases organize data into nodes and edges.

Key properties

SQL has several properties that distinguish it from NoSQL. First, its primary focus is managing relational database systems:

• The Data Definition Language (DDL) of SQL consists of statements for defining and managing database structures
• The Data Manipulation Language (DML) provides statements for manipulating data within the database.
• Additionally, SQL helps maintain “ACID” properties (Atomicity, Consistency, Isolation, Durability) in RDBMS. 

NoSQL also has several properties that differentiate it from SQL. The first key property is its schema-less nature, which offers users greater flexibility in handling data. Unlike SQL, NoSQL does not enforce the use of complex join queries.

Another notable property of NoSQL is its support for distributed architectures.

Database scalability

When it comes to scalability, SQL databases are vertically scalable. This vertical scalability is achieved by increasing the hardware capacity, like memory or CPU power. However, vertical scaling can be quite expensive as the volume of data grows.

SQL databases also support horizontal scaling, adding more database instances and distributing them across multiple machines or servers.

NoSQL databases inherently support horizontal scalability. Users can leverage NoSQL databases to handle massive amounts of data. It can be accomplished by adding more servers to the database cluster in a distributed manner and using data partitioning strategies.

Thus, NoSQL databases are typically well-suited for highly scalable applications.

Community support

When considering the community support for each database type, SQL has broader community support due to its well-established community compared to NoSQL. There are many resources to learn SQL, including tutorials, courses, articles and documentation.

Popular SQL databases like MySQL, PostgreSQL and Oracle have large and interactive user bases. They actively engage in discussions and offer valuable support to fellow users.

In contrast, NoSQL is a relatively new technology. Thus, its community is less mature and established than SQL. Yet its community is growing quickly, with specialized communities for different NoSQL databases.

The availability of resources for learning NoSQL is still limited compared to SQL when considering online learning resources. However, the vendor or organization behind a specific NoSQL database often actively supports and engages with the community.

Choosing SQL: Pros & cons

Now that we understand the basics and the key differences, let’s look at the benefits and drawbacks of each database type. This will help you get a sense of which might be the right option for your specific use case.

Benefits of SQL

• Well-established. A well-established, well-documented and widely supported query language.
• Various database query operations. Supports a wide range of database query operations, including joining, grouping, filtering and aggregating data, enabling complex data analytics.
• ACID property support. Help enforce the ACID properties of databases and constraints for strict data consistency and data integrity.
• Data consistency. The schema helps promote data consistency.
• Easy to use. Easier syntax and structure. Thus, SQL is easy to learn and use for querying and manipulating data.
• Simple. Useful for simple aggregations and ETL jobs.

SQL drawbacks

• Less flexible. Schema is less flexible as it supports only structured data. That means using complex data structures can be challenging in traditional SQL databases.
• Fixed schema. SQL databases feature a fixed schema. Thus, the structure of tables and relationships must be defined beforehand — and it will be difficult to alter it later.
• Query degradation. SQL query performance can degrade as the volume of data grows. That means you may have to use query optimization techniques to improve its performance.
• SQL complexity. Although SQL has an easy syntax, some database operations may involve complex queries that require significant expertise and experience.
• Debugging complexity. Debugging for errors may be difficult or time-consuming in SQL as it lacks informative error messages.
• Limited scalability. The scalability of SQL is somewhat limited compared to NoSQL. 

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Opting for NoSQL: Benefits, drawbacks

Pros of SQL

• Flexible schema. The schema in NoSQL databases is highly flexible. Thus, there is no need for a predefined schema, allowing users to easily modify the schema as they need.
• Various data models. Allow users to work with various data models, such as key-value pairs, documents or graphs.
• Complex data types. NoSQL databases offer robust support for complex data types. It simplifies working with diverse and unstructured data formats commonly encountered in modern applications.
• Speed. Provide fast and optimized queries.
• Horizontal scaling. NoSQL databases facilitate horizontal scalability for handling large amounts of data by distributing it across multiple servers.
• Open-source options. Several NoSQL query languages are open-source, which can significantly reduce costs.

Cons of NoSQL

• Less mature compared to SQL, relatively less community support.
• Different query languages. Since different NoSQL databases have different query languages, you must learn the specific language of each NoSQL database you work with. 
• Fewer resources. NoSQL query languages may have a smaller ecosystem and fewer available tools than SQL. 
• No data duplication. NoSQL lacks support for data duplication. Hence, such databases can be larger than SQL databases.
• Could require multiple databases. Since specific NoSQL databases are designed to support particular use cases, it can be difficult to achieve all the required use cases using a single database.

When to use SQL vs. NoSQL

OK, so with all of these details and pros/cons, which database is right for you right now?

Applications of SQL

SQL is ideal for the following scenarios:

• If your application has a well-defined, structured schema.
• Applications that need complex queries, such as nested queries, joins and aggregations. 
• For data analytics, reporting and business intelligence applications.
• Applications that do not require higher scalability. 
• SQL is well-suited if your application needs ACID compliance, such as financial applications, stock control applications, etc.

(Explore the data analyst role & top data analytics certifications to earn.)

Applications of NoSQL

• On the other hand, NoSQL databases are more suitable for the following scenarios:
• If you are dealing with unstructured or semi-structured data.
• For applications that require frequent schema changes.
• Applications that require high scalability and the storage and processing of a large amount of data.
• Processing graphs or hierarchical data.
• Processing log files and sensor data.

Summarizing SQL vs NoSQL

SQL and NoSQL are two different database technologies, each with its own set of strengths and weaknesses. SQL requires structured and tabular data and supports complex queries. On the other hand, NoSQL offers flexibility, scalability, and support for various types of data. SQL has widespread community support. Yet, community support for NoSQL is somewhat limited as it is a relatively new technology.

Likewise, there are significant differences between SQL and NoSQL as discussed in this article. So, it is crucial to understand your database requirements before choosing the appropriate technology for your project.

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