If you have ever heard the term ‘NoSQL database’ and it sounded complicated, don’t worry. At Doowebs we will explain in a simple way what a NoSQL database is, how it differs from traditional databases and why they are so popular nowadays.

What is a NoSQL database?

A NoSQL database is a type of database designed to store, retrieve and manage large amounts of unstructured or semi-structured data. The term ‘NoSQL’ comes from ‘Not Only SQL’, which means that, although they are not based on the traditional SQL language (Structured Query Language), they are not necessarily against SQL.

Instead of following a rigid, structured approach like relational databases (SQL), NoSQL databases allow greater flexibility in organising and managing data.

Why did NoSQL databases emerge?

Relational databases have been the norm for decades, and for many types of applications they remain an excellent choice. However, with the growth of the digital age and the explosion of data generated by web applications, social media, mobile devices, and large technology companies, new needs have arisen.

Traditional SQL databases faced significant challenges in handling massive amounts of unstructured data, such as images, videos, social media posts or sensor data.

It was then that NoSQL databases emerged, designed specifically to handle these types of data in a more efficient and scalable way.

Unlike relational databases, which require data to be stored in well-defined tables with rows and columns, NoSQL databases allow data to be stored in more flexible structures.

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Comparison between NoSQL and SQL databases

Structure

SQL databases: In relational databases, data is organised in tables, where each table has a fixed structure of rows and columns. Each row represents a data record, and each column a field with a specific data type (e.g., numbers, text, dates, etc.).
NoSQL databases: NoSQL databases do not follow this rigid table schema. Instead, data can be stored in a variety of forms, such as documents, graphs, key-values or columns. This provides much more flexibility to handle more complex and changing data types.

Scalability

SQL databases: Relational databases generally scale vertically. This means that when a database needs more resources, there is a tendency to upgrade the hardware of the server where it is hosted (more memory, more processors, etc.).
NoSQL databases: NoSQL databases are designed to scale horizontally. Instead of relying on a single server, they can distribute data across multiple servers, making them ideal for applications that handle large volumes of information or need to grow rapidly.

Query language

SQL databases: They use the SQL language to query and manipulate data. SQL is a very powerful language, but can be somewhat complicated to learn and use for those unfamiliar with it.
NoSQL databases: They do not use SQL as a standard query language. Instead, each type of NoSQL database has its own way of interacting with the data, which may vary from vendor to vendor.

Data types

SQL databases: These are ideal for structured data, where each piece of data fits into a specific format (e.g., a customer table with columns for name, address and phone number).
NoSQL databases: These are best suited for unstructured or semi-structured data, such as social media posts, server logs, or data from sensors. They do not require a fixed format and can handle changes in data structure without major adjustments.

NoSQL database types

Within the NoSQL world, there are several types of databases, each designed to handle different types of data and needs.

Document databases

Document databases store data in document format, usually in JSON, BSON or XML. Each document is independent and can have its own structure, which gives great flexibility.

Typical use: Applications that handle large volumes of unstructured data, such as online shops or social networks.

Key-value databases

This type of database is one of the simplest. Data is stored as key-value pairs, where a unique key is associated with a specific value. They are extremely fast for searching information by key.

Typical use: Caches, session systems, or applications that need ultra-fast responses.

Columnar databases

Columnar databases store data in columns rather than rows. This makes them more efficient for certain analytical queries and for handling large amounts of distributed data.

Typical use: Analysis of large volumes of data, such as in Big Data environments.

Graph databases

This type of database is used to model complex relationships between data. Networks are made up of nodes (entities) and edges (relationships). They are especially useful for analysing connections and relationships in networks.

Typical use: Social networks, recommendation engines, fraud analysis.

Advantages of NoSQL databases

Scalability: They are ideal for applications that need to grow quickly and efficiently, as they allow data to be distributed among several servers.
Flexibility: No rigid schemas are required. This allows developers to adjust data models as needed, without the need to make major changes to the database.
High availability: Many NoSQL databases are designed to be fault-tolerant, meaning that they can continue to function even if a server goes down.
Performance: For certain types of applications, NoSQL databases can be much faster than traditional SQL databases.

Common uses of NoSQL databases

Social media: Platforms such as Facebook and Twitter generate huge amounts of unstructured data in real time, making NoSQL databases an ideal choice.
E-commerce: Online shops like Amazon need to manage product catalogues that can change constantly, something that fits well with the flexibility of NoSQL.
Data and analytics: When it comes to analysing large volumes of data (e.g. server logs, sensor data, etc.), NoSQL databases can handle these loads efficiently.

NoSQL databases offer a flexible, scalable and efficient alternative to traditional SQL databases. Although they do not replace relational databases in all cases, they are especially useful for applications that need to handle large amounts of unstructured data or where scalability and performance are important.

Implement NoSQL efficiently with Doowebs

To conclude this analysis of NoSQL databases, it is important to emphasise that at Doowebs we are committed to offering technological solutions adapted to modern needs.

Our team can guide you in the implementation of NoSQL databases if your application or business requires handling large volumes of unstructured or semi-structured data in an efficient and scalable way. Whether you need flexibility, horizontal scalability or the ability to analyse complex data, at Doowebs we help you take advantage of NoSQL to maximise the performance of your applications. Contact us for a customised solution!