Relational Databases vs. Non-relational Databases: Complete Information
Both relational and non-relational databases share a high volume of the market. From relational databases, MySQL is top on the list and shares great volume, while MongoDB does not when it comes to non-relational databases.
The core difference between both of these databases is that relational databases don’t show compatibility with semi-structured or un-structured data and JSON or BSON type file formats. In contrast, non-relational databases show compatibility with both of these file formats and even unstructured data.
To know more about the detailed differences between both of these databases, stay with this page and be good to go. Let’s dive right into this.
What are Relational Databases?
Relational databases are one of the most popular database management systems that store and organize your data using structured tables. These databases follow relational model principles proposed by Edgar F. Codd and help you specify relationships between entities present in the tables.
Relational databases use primary and foreign keys, different types of commands for operations, and unique constraints to ensure perfect validation of your data. These databases use SQL as a structured query language for maintaining and manipulating data, complex queries, data integrity, and more.
Explore some benefits of relational databases below.
Benefits of Relational Databases
There are several benefits relational databases offer. We have mentioned the core ones below that can actually help you ensure project success.
Efficient Data Integrity
Using relational databases, you ensure the best data integrity. Relational databases offer essential commands, allow you to utilize primary and foreign keys, and introduce unique constraints which you can check for the rules accuracy that you have specified for data relationship.
Normalization Process
Relational databases break down your large datasets and promote table partitioning with the normalization process. This reduces duplicity and avoids inconsistent and invalid data entry. You use less storage and get organized data in this way.
Data Entities in Tables
Relational databases use relational model principles and organize your data into structured tables (rows and columns). These languages use SQL as a structured query language, so you can use keys and constraints to build connections between your data and easily access any specific part of your database.
ACID Properties Compilation
Relational databases use ACID properties (Atomicity, Consistency, Isolation, and Durability) which is great. You can ensure data validation, data modification, data integrity, and other features with this. This is also called the key feature of relational databases differentiating them from all other types of databases.
Horizontal and Vertical Scalability
Relational databases provide options for both scalability types: horizontal and vertical. In terms of vertical scalability, you can add more powerful hardware to the single server. While in terms of horizontal scalability, the load can be divided into multiple servers or nodes.
In addition to this, other features like table partitioning, reduced duplicity, caching mechanism, fast indexing, normalization process, and better query optimization promote higher scalability.
Limitations of Relational Database
Along with the benefits, relational databases also come up with some limitations or disadvantages. Let’s also discuss them below.
Schema Issues
Relational databases organize your data into tables for better grouping, summarization, and management. They are good only for medium-sized projects, but
Schema features of relational databases are good for structuring your data but sometimes it causes issues. You face issues in table partitioning and scaling the project vertically or horizontally. With other NoSQL or non-relational databases, you might change or modify the schema later but relational databases cause issues when you try to change schema later.
Complex Queries Issues
Relational databases are not good for complex queries sometimes. Especially, when you’re working on enterprise-level applications or have large-sized data to be processed and partitioned in the tables. To enjoy maximum speed and performance, don’t make complex queries.
Incompatibility with Semi or Un-structured Data
Relational databases primarily support structured datasets. If you have large volumes of unstructured data to be processed, relational databases might not be a good fit for you. Additionally, data such as documents, images or other multimedia files might also not be compatible with relational databases.
Vendor Lock-In
When starting with relational databases, you need to choose a vendor first which may lead to vendor lock-in. It’s a kind of irritating situation because when you switch to other vendors in the future, you might need to pay more costs.
Schema Rigidity
Relational databases follow fixed schema patterns, so you need to define the structure of your data exactly before inserting your data. If you don’t, changing or improving the schema later might irritate you. If fixed schema is your priority, it's kind of a good feature for you. Most people don’t like it because they prefer improving schema from time to time.
What are Non-relational Databases?
Non-relational databases are often called NoSQL databases. They don’t use traditional relational database principles and models, although they allow to storage and retrieval of data using various models. These databases are the best option when you have unstructured data to be processed.
With these databases, you can easily opt for higher scalability and make your data compatible with various file formats such as XML, JSON, and BSON. If we describe some best and top-rated non-relational databases, they are MongoDB and CouchDB.
Let’s explore the core benefits of these databases in detail below.
Benefits of Non-relational Databases
Non-relational databases come up with great benefits and you can enjoy them.
Let’s discuss them one by one in detail.
Schema Flexibility
Unlike relational databases, these databases offer flexible schema. The structure of each entry or data record can be different and allows developers to modify the schema from time to time. Meanwhile, you can make your applications or softwares to be stay updated.
Compatibility With Unstructured Data
Relational databases are highly suitable when you have large volumes of unstructured data and semi-structured data like XML and JSON to be processed. You don’t need to follow traditional models or principles. While relational databases only support structured data sets which is considered a limitation here.
Horizontal Partitioning
Non-relational databases support horizontal partitioning and you can easily distribute your data across multiple servers based on specific criteria. You can also enable parallel processing of data queries.
Rapid Development Procedure
Non-relational databases offer a relaxed development process and you can streamline your overall development procedure. Especially, during the application development, we found that non-relational databases are quiet helpful because they allow us to use various tools and resources.
Cost Effective Scaling
Non-relation databases promote horizontal scaling which is a cost-effective scaling option compared to vertical scaling. Most businesses promote non-relational databases because of this feature.
Limitations of Non-relational Databases
Along with the benefits, non-relational databases also come up with some disadvantages. Check them out.
Lack of Standardization Query Language
Non-relational database management systems don’t use any standardized query language comparable to the Structured Query Language in rational databases. Each non-relational database type may have its query language. You might face the challenge of switching between different systems with these databases.
Limited ACID Compliance Features
Non-relational databases prioritize higher horizontal scalability and performance but do not follow ACID properties (Atomicity, Consistency, Isolation, Durability) on priority. It may impact the processing of transactions and other management features.
Poor Support For Complex Queries
In relational databases, we generally find issues with complex queries. We cannot easily manipulate data or have to put hard efforts into joining complex operations. This usually happens when we work on large databases.
Now, if you’re confused about what database type is right for you. Here’s how you can identify which database type is right for you.
Relational Databases vs. Non-relational Databases: Which One is Right?
Well, they both are good and allow you to enjoy the maximum benefits. But the thing is, choosing anyone between relational and non-relational databases depends upon your project’s specific requirements and needs.
Meanwhile, if structured data is your priority and you want relationships between your data and benefits like ACID properties compliance, perfect data integrity, keys, constraints, and commands, go with relational databases.
In contrast, if you have unstructured or semi-structured data to process and you want great compatibility with various file formats including JSON, BSON, and XML along with the implementation of different models and principles, choose non-relational databases.
It's up to you. Understand your project’s specific needs and requirements first to decide which database is going to be the right database for you.
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Conclusion
We promoted a great comparison: relational databases vs. non-relational databases above in the article and described the major differences, advantages, limitations, and more about both types of databases. You can decide which database is better for you after analyzing the detailed comparison between them.
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FAQs
What Is an Example Of A Non-Relational Database?
MongoDB is the best example of a non-relational database. This database is suitable for semi-structured and unstructured data, and also shows compatibility with various file formats including JSON, BSON, and others. Plus, it’s also an excellent choice for large-scale projects.
What Are The 4 Types of Databases?
Talking about the four types of databases, include relational, object-oriented, graph-based, and non-relational databases. Examples of object-oriented databases are those databases that store data as objects, and databases that create relationships between entities using graph structure.
Relational databases examples are PostgreSQL and MySQL. While non-relational database examples include MongoDB.
Is SQL Relational or Non-Relational?
SQL is a structured query language used by relational databases, especially MySQL to manage querying systems and tasks like updating and managing structured data. The language enhances the functionality and management of the overall database.
Is MySQL A Relational Database?
Yes, MySQL is purely a relational database management system (RBDMS). Using relational database principles, ensures the complete organizing and structuring of your data, defining relationships between tables, and more.
Is PostgreSQL a relational database?
Yes, PostgreSQL is also a relational database management system. It supports data organization, simple and complex querying systems, efficient indexing, and transaction management. PostgreSQL is very suitable for large-scale projects.
