In the ever-evolving landscape of database technology, choosing the right Database Management System (DBMS) is crucial to the success of your project. With the two main directions being SQL (relational) and NoSQL (non-relational) databases, it's important to understand their fundamental differences, strengths, and weaknesses.

In this comprehensive article, we delve into the world of SQL and NoSQL, exploring their design, data types, storage methods, and popular choices within each category. We also compare their approaches in terms of structure, queries, scalability, and support to help you make an informed decision based on your project's unique needs. Ultimately, we highlight the importance of a balanced and integrated approach, combining the best of both worlds to unlock the full potential of your database solution. So, read on to discover which DBMS direction is the perfect fit for your project and gain invaluable insights to guide your decision-making process.


A relational database (SQL) is a database where data is stored in the format of tables. They are strictly structured and linked to each other. A table has rows and columns, each row represents a single record, and each column represents a field with a data type assigned to it. In each cell, information is written according to a template.

Non-relational database (NoSQL) stores data without clear structure. The way data is structured eliminates restrictions on storing and using information. Instead of structured tables, there are many different documents inside the database, including images, videos, and others. Unlike relational databases, NoSQL databases do not support SQL queries.

Relational Database Management Systems


  • The data you work with is structured and the structure is not updated frequently.
  • Relational databases, unlike non-relational ones, comply with ACID, requirements for transactional systems.
  • The physical data structure does not correspond to the object model in the application.
  • RDBMSs use SQL queries to work with data.
Relational Database Management Systems

Data in Relational Databases

Data is always stored in tables that look like this.

Data in Relational Databases

Best SQL Database Management Systems

Best SQL Database


MySQL is one of the most popular open-source relational databases. It supports many types of tables, there are a huge number of add-ons and extensions that make it easier to work with the system.

Easy installation can be integrated with other DBMS, as well as integration with MySQL is available in any CMS, framework, programming language. Among the minuses - not all tasks are performed automatically, if something is necessary is not included in the functionality, time for revision is useful, there is no built-in OLAP support.

MySQL is available as a cloud service - in the cloud, you do not need to spend a lot of time deploying and configuring a DBMS. MySQL server is worth choosing at the start of a business to test a hypothesis at a cost or for small projects, as a general-purpose transactional database.

PostgreSQL is the second most important open-source SQL database. It has many built-in features and add-ons, including for cluster scaling and table sharding. Supports JSON/BSON, allowing flexibility in data schema.

It is stable, it is almost impossible to break it or something in the tables.
Of the minuses - complexity requires experience from users. Also, the speed of work may drop during batch operations or during read requests.
PostgreSQL can also be deployed in the cloud - unlike MySQL, it is suitable for large and large-scale projects. It should also be selected if data errors are unacceptable or if there are database requirements such as geodata support. Various PostgreSQL applications implement many specialized queries.

Non-relational Databases


  • The NoSQL database does not restrict the types of data stored. Moreover, if necessary, in the process of work, you can add new data types.
  • If you are developing a system using agile methods, using a relational database can slow things down. NoSQL databases do not need the same amount of preparatory work that relational databases usually need.
  • Use of cloud computing and storage. Cloud storage is a great solution, but it requires data to be easily distributed across multiple servers to scale. Using local hardware for testing and development and then moving the system to the cloud where it works is exactly what NoSQL databases are designed for.
  • Physical objects in NoSQL can usually be stored directly in the form in which the application then works with them.
  • NoSQL DBMSs do not support SQL queries and use different approaches to work with data.

Best NoSQL Database Management Systems

Firestore is a flexible, scalable NoSQL cloud database to store and sync data. It keeps your data in sync with client apps through realtime listeners and offers offline support, so you can build responsive apps that work regardless of network latency or Internet connectivity.

Firestore also features fast and rich queries and scales and offers seamless integration with other Firebase and Google Cloud products.

You actually don’t need additional backend dev to rule Firestore. Our mobile devs can work with Firestore themselves😉 

Once you've chosen the right database for your project, make sure to also check out our comparison of Flutter vs React Native: Which Framework Wins in 2024? to help you decide on the best mobile development framework.

If you're considering developing a cross-platform app using the Flutter framework, our team at What the Flutter specializes in creating high-quality, responsive mobile applications tailored to your needs. In fact, you can learn more about our flutter app development services to get a better understanding of how we can assist you. Feel free to reach out to us for expert guidance and support throughout your app development journey.


MongoDB is an open-source document type database. It can work with both structured and unstructured data. However, performance issues can occur when working with relational data models. Suitable for projects that work with different data that is difficult to classify, or if a significant change in the data structure is expected in the future, including for OLAP scenarios.

This database scales well horizontally without loss of speed, is easy to use, performs well, and is suitable for large amounts of data. It is easy to install and try many settings. Of the minuses - it does not use SQL as a query language, there are tools for translating SQL queries, but they require configuration. There is also no data connectivity. MongoDB is difficult to maintain because it requires NoSQL experience.

MongoDB is convenient to use in the cloud, as there are fewer configuration and management issues. This is a solution for caching data, storing documents, content and other unstructured data, working with big data and machine learning, message queues.


Redis can be used as a standalone DBMS for fast work with small amounts of data or as a caching layer for working with another DBMS. Helps speed up a slow database, increases the speed of query processing. For example, you can use MySQL as the main database, and Redis for the cache. It is able to work with different types of data, quickly processes them in memory, can store them on disk, and is distinguished by simple data replication. Of the minuses - difficulties when working with large amounts of data, risk of not saving data, difficulties with setting up a cluster and sharding. All these problems are solved when running a configured Redis DBMS in the cloud, where the provider takes care of support, hosting and data backups.

Types of NoSQL Database Structures

Document Databases

Data is stored in collections of documents, typically using JSON, XML, or BSON formats. One record can contain as much data as needed, in any data type (or types) - there are no restrictions. Within one document there is an internal structure. However, it may differ from one document to another. Documents can also be nested inside each other.

That is, instead of columns and rows, we describe all the data in one document. If we needed to add new data to a relational database table, we would have to change its data schema. In the case of documents, you only need to add additional key-value pairs to them.

An example of such a database is MongoDB.


Key-value Databases

Each record has a key and a value. Developers mostly use such databases when the data is not too complex, and speed is a priority. For example, it's a great choice for storing configuration data. No schema is assigned to the stored data, and the database itself is much lighter than a relational database.

An example of such a database is Redis.


Graph Databases

Graph databases consist of nodes and links between them. Nodes represent the elements in the database, and links between them define their relationship with each other. Of all the types of databases, this is the best option in cases where various relationships between data are a priority.

However, graph databases have a clear disadvantage: although you need a query language to access data, you can't use SQL or any other common approach. The lack of standardization means that most query languages can only be used in one or a few types of graph databases.

Examples of such databases: Neo4j, OrientDB.

Neo4j, OrientDB

Columnar DBMS

Columnar DBMS are a good option for processing big data, they are characterized by high performance, efficient data compression and excellent scalability.

In such systems, data is stored in the form of a matrix, the rows and columns of which are used as keys. Like a table, a column family contains columns and rows. However, there is a distinct difference: a column does not span all rows. Instead, it is contained within a row, which also means that different rows can have different columns.

Apart from the columns, each row also has an identifier called a key, and each column contains a name, a value, and a timestamp. Simply put, in a columnar database, data is also stored in a table. It only consists of a set of columns, each of which, in fact, is a separate table.

This is necessary so that you can quickly get data from the database for analysis. For example, if you want to extract the amount of the average check of a client from a relational DBMS, you will have to look for this value in each row, and in a columnar DBMS, you can immediately take information from the desired column.

An example of such a database is Cassandra.


SQL & NoSQL Approaches

  • Structure and type of stored data: SQL databases require a uniquely defined data storage structure, while NoSQL databases do not impose such restrictions. Queries: regardless of the license, RDBMS implements SQL standards, so you can get data from them using the SQL language. Each NoSQL database implements its own way of working with data.
  • Scalability: both solutions are easily scaled up vertically (for example, by increasing system resources).  However, due to their modernity, NoSQL solutions are much more advanced in the horizontal scaling direction. (for example, creating a cluster of several machines).
  • Support: RDBMS has a very long history. They are very popular and therefore it is very easy to get support, paid or not. Therefore, if necessary, it is much easier to solve problems with them than with NoSQL, especially if the problem is complex in nature (for example, when working with MongoDB).
  • Storing and accessing complex data structures: By their very nature, relational databases are designed to handle complex situations. NoSQL approaches can't work with complex data.
  • Indexing: In both SQL and NoSQL databases, indexes serve the same purpose - to speed up and optimize data retrieval. SQL indexes are represented as B-trees, which reflect the hierarchical structure of relational data. In NoSQL databases they point to documents, or parts of documents, between which there is basically no relationship.

So, What is Your Choice Then?

What is Your Choice Then?

Here are the signs of projects for which SQL databases are ideal:

  • Data is structured and is not supposed to be changed frequently.
  • The project uses a complex data storage structure.
  • The main requirements of the project - reliability and safety.
  • DB needs to comply with ACID requirements.

And here are the properties of projects where NoSQL is suitable:

  • Data requirements are fuzzy, undefined, or evolving as the project evolves.
  • The goal of the project can be adjusted over time, while the ability to immediately start development is important.
  • One of the main requirements for a database is data processing speed and scalability.
  • The project needs to be developed quickly.


The choice between SQL and NoSQL databases ultimately depends on the specific requirements and goals of your project. Both relational and non-relational databases have their own unique strengths and weaknesses, making them more suitable for certain scenarios. While SQL databases excel in handling structured data and ensuring reliability and safety, NoSQL databases provide flexibility, speed, and scalability, making them ideal for projects with evolving data requirements or rapid development timelines. So, take the time to carefully assess your project's needs, consider the strengths of each database type, and make an informed decision that will set the foundation for your project's success.

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