SQL vs NoSQL Databases: 12 Major Differences, Advantages, and Use Cases

Introduction

Databases are vital to modern computing. They are used to store, organise, search and manage information. Almost all digital services, from banking systems, online shopping systems, social media sites and business management systems will use databases to store massive volumes of information. The two most common types of databases used are SQL and NoSQL. Whilst they both have the same basic purpose, there are differences to how they store information. 

As developers often look to choose between traditional relational databases and modern NoSQL database systems to see what best fits their needs in terms of flexibility, performance, consistency and scalability, this article will look at the 12 Major Differences between SQL and NoSQL databases. It will also cover what the benefits and drawbacks are, examples of popular databases and scenarios where each is the best choice.

SQL Databases

SQL (Structured Query Language) is a programming language used to interact with relational database management systems. The information is stored in tables with rows and columns to make it easier to store structured data. Each table will represent a particular category of information. 

Columns will represent the attributes of the information and rows will represent individual records. For example, an online shopping system may have separate tables for customers, products, orders and payments. This information can be linked together using primary and foreign keys. SQL databases are great when dealing with structured information and when accuracy is required.

How do SQL Databases Work?

SQL databases use a defined schema to store information. This will define the table names, columns, data types, relationships and constraints for the information. The application will send SQL commands to the database management system. There are many commands available. Some common ones are SELECT to find records, INSERT to INSERT information, UPDATE to UPDATE records and DELETE to DELETE records. 

Relational databases can also use JOIN commands to combine information from different tables. This makes SQL very useful when accuracy is needed, information needs to be organised and relationships need to be maintained. The database engine will process the queries, check any constraints and return the results.

Popular Examples of SQL Databases

There are many SQL databases available.

MySQL – MySQL is a popular open source relational database management system. It is often used on websites, content management systems and web applications. It is known for being easy to use, fast and works with several programming languages. It is often used in applications developed using PHP.

PostgreSQL – PostgreSQL is an advanced open source relational database. It is known for being reliable and having advanced features. It has many features such as JSONB, indexing, advanced data types and transaction management. This makes it suitable for business applications and data heavy applications.

Microsoft SQL Server – Microsoft SQL Server is a relational database developed by Microsoft. It can be used for business applications, enterprise systems and managing data. It has many features such as transaction processing, reporting, analytics, security and integration with other Microsoft technologies.

NoSQL Databases

NoSQL databases are a type of non-relational database management system. They store and process information in a way that does not use traditional relational tables. The name is often taken to mean ‘Not Only SQL’ as some NoSQL databases can use SQL queries. NoSQL databases can store information in different ways. Unlike traditional relational databases, information can be stored as documents, key value pairs, graph databases or wide column databases. 

This makes them great for applications that need to store varied data types and frequently changing information. For example, a social media site may need to store user profiles, images, messages, comments and activity logs. NoSQL databases can handle this varied information.

How do NoSQL Databases Work?

NoSQL databases use different methods of storing information depending on the database and application. Rather than using defined tables, they will store information in the best format for accessing it. Some databases store full records in JSON like documents whilst others store information using unique keys. Wide column databases store information in column families whilst graph databases store relationships between information. 

Many NoSQL databases are designed to store information across multiple servers to allow the application to scale. How consistent, transactional and replicated they are will vary greatly. Some will be very consistent and transactional whilst others will be eventually consistent.

Popular Examples of NoSQL Databases

There are many NoSQL databases available.

MongoDB – MongoDB is a document based database. It stores information in BSON documents (binary representation of JSON like data). It has a flexible document structure so developers can manage different fields. It is often used for content management, product catalogues, user profiles and web applications.

Redis – Redis is an in-memory data store. It allows key value pairs to be stored. It is often used to cache information, manage sessions, create real time leaderboards and for fast Data Retrieval.

Apache Cassandra – Apache Cassandra is a distributed wide column database. It is designed to handle large amounts of data across multiple servers. It has high availability and can handle continuous data capture, distributed applications and large amounts of data.

12 Major Differences between SQL and NoSQL Databases

There are many differences between the two databases. This will help developers decide what type of database to use.

1. Data Structure

The first major difference between SQL and NoSQL databases is how the information is stored. SQL databases store information in relational tables using rows and columns. Each table will represent a specific entity such as customers, employees and products. Relationships between tables can be made using primary and foreign keys. NoSQL databases can store information in many formats such as documents, key value pairs, wide column and graph databases. 

For example, MongoDB will store related data in documents whilst Redis will store information using keys. NoSQL databases can handle applications with varied data requirements. SQL is generally a better choice when there are clear relationships between information whereas NoSQL provides more flexibility.

2. Flexible Schemas

SQL databases generally require a defined schema. The developer will need to define the tables, column names, data types and constraints. This can help keep things consistent. However, if changes need to be made to the database structure then this may require migrations. 

NoSQL databases often have more flexible schemas. Different records in the same collection can have different fields. For example, an online marketplace could have products with different attributes depending on if they are an electronic, clothing or furniture item. This can be handled using a document database. However, they still need to have proper data validation.

3. Scaling

Scaling refers to how well a database can handle more information, users and application requests without causing issues. Traditional SQL databases often scale vertically. This means increasing the processing power, memory and storage on the server. Some relational databases can scale horizontally using replication, partitioning and sharding. 

However, this may require more thought about the architecture. Many NoSQL databases can scale horizontally. This means distributing the information and processing across multiple servers. This means organisations can scale their systems. Apache Cassandra can store distributed data across multiple nodes. However, this doesn’t mean every NoSQL application will be faster or cheaper. How the database is designed, the network, workload and complexity will all affect how well it can scale.

4. Consistency

Consistency refers to how accurate the database is when creating, updating and retrieving information. SQL databases often support ACID transactions to help ensure the database is reliable. Many NoSQL databases were originally designed to focus on availability and scaling. They may have inconsistencies between the replicated data. 

This is often referred to as eventual consistency. This means that updates won’t appear across all the nodes straight away. However, this shouldn’t be assumed. Modern NoSQL databases can have strong consistency options. Organisations should look at what consistency their application requires. Financial data will generally require careful transaction management. However, some content applications can cope with some delay.

5. Transactions

Transactions are a group of operations that are treated as one unit. SQL databases are well established for transactions. They often support ACID properties. This means they can complete transactions reliably. For example, if a banking application was moving money from one account to another, the money needs to be moved from one account and added to another. 

NoSQL databases also support transactions. However, this will vary between databases. MongoDB can support multi-document ACID transactions. Other NoSQL databases will focus on operations relating to individual records or partitions. Developers should be aware of what transaction features their chosen database will have. Choosing the correct one will depend on how many records need to be updated and how strict you want your application to be.

6. Query Languages

SQL databases use a Structured Query Language. This allows developers to SELECT information, filter, JOIN, group and calculate. This makes it a good choice for applications with complex relationships. NoSQL databases don’t have one universal query language. Each database will have its own query methods. MongoDB uses document based queries and aggregations. 

Redis has commands to manipulate the stored values. Apache Cassandra uses the Cassandra Query Language (CQL). Developers will need to learn how to query their chosen NoSQL database. Whilst knowledge of SQL can be transferred to many relational databases, NoSQL query skills may be more closely related to the actual database.

7. Performance

Performance will vary depending on several factors. SQL databases can be efficient when dealing with structured data, complex queries, joins and transactions. They can use query optimisers to analyse how best to get the information. NoSQL databases can be very efficient depending on the workload. If the information is structured in a predictable way then they can be very quick. For example, Redis can be very quick at retrieving frequently requested information as it mainly works in memory. 

MongoDB can be efficient when retrieving documents using the correct indexes. Cassandra is designed to distribute large workloads across multiple nodes. However, this doesn’t mean that NoSQL databases will always be faster. If a NoSQL database isn’t well designed then it may not be as efficient as a well optimised relational database. Complex queries in relational databases can become expensive when dealing with large data sets and multiple joins.

8. Relationships and Joins

Relational databases are specifically designed to manage relationships between different categories of information. They use primary keys, foreign keys and JOINs. For example, an education management system may have different tables for students, courses, teachers and exam results. They can be linked using identifiers. This helps to avoid unnecessary duplication. 

NoSQL databases often use different methods for relationships. Document databases may store related information within the document or link to other records. Graph databases use nodes and edges to show relationships. Some NoSQL databases can query relationships but they won’t be the same as relational joins. It will depend on if your application needs structured relationships, embedded records or complex network relationships.

9. Data Types

SQL databases are generally designed for structured data. This means there will be clear data types. Common data types include integers, decimals, text, dates and booleans. This helps the database validate the data and make accurate calculations. NoSQL databases can store structured, semi-structured and some unstructured data depending on the database. 

Document databases can store nested objects and arrays. Graph databases can store entities and relationships. Wide column databases can store records with varying column structures. This can be useful for applications that gather information from multiple sources or frequently changing data. However, they will still need to have a good data model, validation and access methods.

10. High Availability

High availability refers to how accessible a system is if there are issues with the components. Fault tolerant means that the system can still run despite some hardware/software/network failures. Many NoSQL databases can store data across multiple servers. This means the application can still run if individual nodes fail. Apache Cassandra can replicate data across nodes and can be set up to be highly available. 

SQL databases can also have high availability features. They can have replication, backups, clustering and failover. The difference is the architecture and how they need to be set up. Organisations should look at how they will recover, how the data will be replicated, how much downtime they can have and the risk of losing data.

11. Security

Security is important for both SQL and NoSQL databases as they often store sensitive company data, customer data and application data. SQL database platforms often have security features such as authentication, authorisation, role based access, encryption, auditing and more. This means the administrator can control who can access certain tables, records and database actions. 

NoSQL databases also have security features. MongoDB has authentication, role based permissions, encryption options and auditing. Redis also has authentication and access features. Whatever database you choose you will need to ensure the security is set up correctly. You will need to secure your credentials, apply updates and limit access. This shouldn’t be assumed. It will depend on what database you choose, how it is deployed, how it is set up and managed.

12. Cost, Maintenance and Complexity

The cost of a database will depend on the licence, infrastructure, storage, maintenance, administration and development. Many SQL platforms will have mature management tools, lots of documentation and established development methods. Open source options such as MySQL and PostgreSQL can reduce the licence costs. However, you will still need to plan for the infrastructure and administration costs. 

NoSQL databases will have open source and paid options. The cost will depend on how big you want to deploy it, how you are hosting it and what you need. Distributed NoSQL databases may require more knowledge about replication, partitioning, consistency and clusters. However, the flexible document models can make some aspects easier to develop. Neither option will be cheaper. Organisations should look at the total cost of ownership.

Benefits of SQL Databases

SQL databases have many benefits. They have a relational structure which makes it easy to organise records. As SQL is very popular there will be lots of documentation, tools and a large professional community. One of the main benefits is transactions. ACID properties help ensure data is kept intact when performing multiple related actions. 

They can also perform advanced queries. This means developers and analysts can query multiple tables, calculate summaries and produce detailed reports. The defined schemas and integrity constraints help to stop invalid data from being added. This makes them a good choice for banking applications, inventory management, customer relationship management systems and enterprise systems where accurate and organised records are essential.

Drawbacks of SQL Databases

Whilst there are many benefits there are also some drawbacks. The defined schemas can make it harder to make changes. If there is a lot of existing data or frequent changes to the application then this may be an issue. Complex migrations may need to be carefully planned. Traditional relational databases may struggle to scale extremely large workloads across multiple servers. 

However, modern databases have many techniques available. Applications that have very varied data may require complex table structures or frequent changes to the schema. Relational joins can become expensive when querying many large tables without the correct indexing. This doesn’t mean that SQL databases are outdated. It just means it is important to match the relational structure to the structure, scalability and requirements of your project.

Benefits Of Using NoSQL Databases

NoSQL databases give flexibility and scalability to applications that store large amounts of complex data. This can be done through different storage mechanisms. Document databases can be used for storing nested data, key-value stores can be used for quick lookup, wide column stores can be used to store data from distributed environments and graph databases can be used for interconnected data. NoSQL databases can be used to store data across several servers. This can aid organisations to scale their storage and processing capacity as there will be more data coming into their application. 

Some NoSQL databases can also be used for replication and availability across different geographical areas. As they are very flexible with their data models, they can be useful for applications where the data constantly changes. This means they can be useful for Social Media platforms, real-time applications, product catalogues, recommendation systems, content management platforms and large-scale data processing.

Drawbacks Of Using NoSQL Databases

Whilst there are many benefits to using NoSQL databases, there can also be some challenges which need to be understood. As there are various types of NoSQL databases, there are different query languages and data models to learn. Certain NoSQL databases will require you to plan your data model based on how you want to access your data. This can cause complications if you need to change how you access your data later down the line. 

Managing relationships can be an issue for certain NoSQL databases if your application requires a lot of joins and complex relational queries. As they are distributed, they can be more complicated to manage. This means that administrators need to manage how data is replicated, partitioned, the network and how consistent it is. With flexible schemas, there can be inconsistencies in the data if you do not have proper validation. This highlights that you should think about what specific NoSQL databases offer.

Types Of NoSQL Database Systems

There are several types of NoSQL databases. 

Understanding what type of database you need will help you choose the right one for your application.

Document Oriented Databases

Document databases store data in documents, usually in a JSON format. This can include fields, nested objects and arrays to store related information. MongoDB is a popular document database. It can be used for applications that need to store user profiles, product catalogues, content management and customer information. For example, an online marketplace could store product descriptions, prices, categories, specifications and options in documents. 

Products can have different attributes. Document databases are useful if information varies between records or is constantly changing. You need to consider indexes, size, duplication and updates when building document-based applications.

Key Value Databases

Key value databases store data in pairs. Each piece of data will have a unique key and value. This is quite an easy way to store data and can be efficient for applications that need to frequently access data using known keys. Redis is one of the most popular key value data stores. It can also store other data types. Key value databases are useful for caching frequently accessed data, user sessions, temporary application data and real-time leader boards. 

For example, an E-commerce site could use Redis to store temporary product information that is frequently asked for by customers. This would reduce the amount of times the application would need to query the database. However, they may not be suitable for applications that have complex relationships.

Wide Column Databases

Wide column databases store data in rows and column families. They are designed to be distributed and can handle large amounts of data. Apache Cassandra is a well-known wide column database. It stores data across multiple nodes and has replication capabilities. Wide column databases can be useful for applications that need to store constant flows of data such as monitoring systems, event processing and large-scale operational applications. 

For example, a telecommunications company could have a distributed database to store information from their network equipment across different geographical locations. Cassandra is designed for high write volumes and distributed data. You need to consider partition keys and queries carefully as inefficient data distribution can cause performance issues.

Graph Databases

Graph databases store data in nodes, edges and properties. Nodes represent entities, edges represent relationships between entities and properties give more information about the nodes and relationships. This is useful for applications where it is important to understand the relationships between data. For example, a social media application could have users as nodes and friendships/interactions as edges. 

Graph databases can help to identify mutual connections, suggest new friends or analyse relationships. They can also be used for fraud detection, recommendation engines, knowledge graphs and network analysis. Unlike relational databases that often use JOINs to link data, graph databases are designed to navigate relationships. They will be effective depending on how complex the relationships are and what capabilities the chosen database has.

Examples Of Real World Uses For SQL And NoSQL Databases

Both SQL and NoSQL databases can be used in many industries and digital services. 

They will depend on what type of data you are storing, the number of users, the transaction requirements and what the workload will be.

Banking Applications

Banking systems will rely heavily on having accurate transactions and financial data. This can be achieved by using relational databases. For example, a banking application could use SQL databases to store customer accounts, deposits, withdrawals, transfers and transaction history. Relationships between customers and accounts can be managed using constraints and keys. 

ACID transactions can be used to ensure that related financial transactions are completed. However, financial organisations may also use NoSQL databases to help with caching, monitoring and real-time events. It will depend on what the workload is. Maintaining accurate balances and transaction history is key for financial data so relational databases are often a good choice.

E-commerce Applications

E-commerce applications will need to manage product information, customer profiles, shopping carts, payments, orders and inventory. SQL databases can be useful for managing orders, customer data, payments and other transactional data. The database will have relationships between products, customers and purchases. NoSQL databases can be used for product catalogues, personal recommendations, user sessions and high volume application data. 

For example, document databases can be used to store product information with different specifications. Redis can be used to store frequently requested data. Large online marketplaces may use both to help with performance. This shows that you do not always need to use one type of database. Using different databases can give more flexibility.

Social Media Applications

Social media applications will generate large amounts of data. This can include user profiles, posts, messages, images, comments and more. NoSQL databases can be useful as they can store flexible data and are good for distributed workloads. Document databases can be used to store user profiles and content. Key value databases can be used for user sessions. 

Graph databases can help to analyse relationships between users. SQL databases can be used to store structured data such as account data, subscriptions and admin information. Depending on how large your application is, you may want to use a relational database for a small social media application. However you may need several databases for a larger application.

Healthcare Management Applications

Healthcare applications will need to manage patient records, appointments, lab results, medical history, billing and admin information. SQL databases can be useful as they can store structured data and have reliable relationships between patients, healthcare professionals, appointments and treatments. Transaction capabilities and access control can help organisations to maintain accurate records. 

NoSQL databases may be useful for certain healthcare workloads that need to store large amounts of documents, monitoring data or data from connected medical devices. When choosing a database for healthcare organisations should consider privacy, security, regulations and data integrity. This will depend on how sensitive the data is, what data model the application uses and how reliable they need the data to be.

Internet Of Things Applications

Internet Of Things applications will need to store data from connected devices, sensors, vehicles and industrial equipment. This can generate a constant stream of data. NoSQL databases such as Apache Cassandra can be useful for handling large amounts of timestamped data. 

Cassandra can collect data from multiple locations due to being distributed. Redis can also be used for real time data. SQL databases can be used to store structured device data, user accounts, equipment data and business transactions. Often organisations will use a combination of both.

SQL vs NoSQL Comparison Table

Below is a table highlighting the main differences between SQL and NoSQL databases.

As shown in the table above, neither database is better than the other. SQL databases offer a tried and tested method for structured data and relational transactions. NoSQL databases offer different data models and are distributed. What is most suitable will depend on what your application requires.

How To Decide Which Database To Use

When deciding which database to use you should consider what technical and business requirements you have for your application. Firstly, consider what type of data you want to store. If you have clear records and complex relationships then a relational database may be better. If you are working with frequently changing data then NoSQL databases may be better due to the flexible data models. Consider what transaction requirements you have. 

If you need to update several related records then you may need a strong transactional system. Consider what future growth you expect, how complex your queries will be, what availability requirements you have and what experience your technical team have. You should also consider the cost of infrastructure. A database may perform well initially but can become hard to manage as your application grows. Careful planning will help you avoid unnecessary migrations, costly infrastructure changes and performance issues.

When To Use SQL?

In general, if you are managing structured data and require relationships between multiple records then SQL would be a good choice. If you require transactions to keep your data accurate and consistent then it can be very useful. This would be common in banking systems, accounting applications, inventory management systems and enterprise resource planning. 

If you need complex queries, detailed reporting and reliable data validation then SQL would be a good choice. If your data is structured then relational databases may be beneficial. However you should still consider what scalability features and performance features individual SQL databases offer. Modern relational databases can handle large workloads if they are set up correctly with the right indexes and maintenance.

When To Use NoSQL?

If you are working with varied data types, frequently changing data models or require distributed data then NoSQL should be considered. NoSQL can be useful if you need to ingest large amounts of data, have flexible document storage, quick key based data and complex graph relationships. This could be useful for social media applications, real-time recommendation systems, connected devices and large content management systems. 

However you should consider what features each platform offers. MongoDB, Redis and Apache Cassandra all have different capabilities so cannot be interchanged. Choosing the right data model is key to getting the performance, consistency and maintainability you require.

Can I Use Both?

Sometimes you can use both SQL and NoSQL databases in your application. This is sometimes referred to as polyglot persistence. For example, an online shopping application may use PostgreSQL to store customer accounts, orders and payments. 

They may use MongoDB for more flexible product data. Redis can also be used to cache frequently requested data. This means each database can be used for what they are best at. However using multiple databases can add complexity. Development teams will need to manage different query methods, back-ups, security, monitoring and synchronisation. Only add extra databases if the benefits outweigh the effort.

Future Of SQL And NoSQL Databases

Databases are constantly evolving as more organisations move towards using cloud computing, artificial intelligence, real-time analytics and distributed applications. SQL databases are still important as applications will still require accurate transactions, structured data and complex data analysis. Modern relational databases are now offering better cloud integration, replication, automation and semi structured data. For example, PostgreSQL has JSONB capabilities so you can use JSON documents but still have relational database features. 

NoSQL databases are also evolving with better distributed consistency, querying, security and scaling. Cloud databases make it easier to use different database models. Multi-model databases are also becoming more popular. This suggests that you will start to choose databases based on what workload your application has.

Conclusion

Both SQL and NoSQL databases are important technologies. They offer different solutions for managing data. SQL databases offer structured data, relational data, standardised queries and reliable transactions. Popular SQL databases such as MySQL, PostgreSQL and Microsoft SQL Server are often used for applications that require accurate records, complex relationships and consistent transactions. NoSQL databases offer alternative data models. 

MongoDB, Redis and Apache Cassandra demonstrate how different NoSQL databases can be used for different purposes. Understanding the 12 key differences will help developers and organisations to understand the pros and cons of using them. Ultimately the best database to use will depend on what data structure, transaction requirements, scalability requirements, performance requirements and maintenance resources you have. In some cases using a combination of SQL and NoSQL will be the most effective solution.