BIMEX Analytics
4 min

Relational (SQL) vs. non-relational (NoSQL) databases: what they are, differences and when to use them

Databases are logical containers that allow organized storage and access to an organization’s data. In this article, we want to explain what they are and the differences between relational and non-relational databases (SQL and NoSQL), and when to choose one type over the other.

History, progress, and the current state of SQL and NoSQL

The real comparison between SQL (relational) and NoSQL (non-relational) has only emerged in the last decade.

Historically, relational databases or SQL-based databases have dominated this sector since their use began in the 1980s and even today remain the most popular choice. However, non-relational databases or NoSQL databases gained momentum from 2012 to 2016, becoming popular in recent years due to the progress of digitalization in companies, IT, and databases. Hence, they are also very popular, although SQL databases remain the most widely used option today.

What are relational databases?

Relational databases (SQL) typically store information in tables containing specific pieces and types of data. Their fundamental principle and core rely on organizing information into smaller chunks that are related to each other through identifiers. Some of the properties associated with SQL databases are atomicity, consistency, isolation, and durability, which means robustness and resilience against failures.

In the design of relational databases, tables usually consist of columns and rows. When new data is added, new records are inserted into existing tables or new ones are added. This allows establishing relationships between two or more tables. Relational databases perform better when the contained data doesn’t constantly change and requires precision.

The most well-known and widely used relational database in this case is MySQL along with Oracle, followed by SQL Server and PostgreSQL, among others.

What are non-relational databases?

Non-relational databases or NoSQL are specifically designed for specific data models and have flexible schemas. They are widely recognized for being easy to develop, functional, and allowing scalable performance.

Unlike relational databases, they don’t have an identifier that serves as a relationship between a set of data and others, meaning they do not store their data in tables. Instead, information is typically organized through documents and is very useful when there isn’t an exact schema of what will be stored, allowing a document to be more detailed and contain a wide variety of information in different formats.

Among their notable properties are flexibility, scalability, high performance, and functionality.

The most globally used and significant non-relational database is MongoDB, followed by Redis, Elasticsearch, and Cassandra.

Moreover, non-relational databases often operate faster as a query doesn’t need to traverse several tables to provide an answer.

Differences between relational and non-relational databases

Before addressing the choice of using a relational or non-relational database, it’s essential to understand what they are and how they differ.

We can say that the main difference lies in the way information is stored. A non-relational database stores data in documents, which tends to be more flexible than the traditional, SQL-based relational database that stores data in tables.

Thus, typically, information and data can be organized in tables or documents. It’s common for table-based databases to be relational and document-based ones to be non-relational… but this isn’t always the case. It’s merely a matter of visualization and knowing how to develop business intelligence.

The most crucial aspect is that, currently, the most competitive databases allow, in one way or another, operations of both types. In other words, the key lies in the appropriate design of the model based on different parameters to fit seamlessly with the suitable database.

When to use SQL or NoSQL?

Both types of databases are highly useful, as we can see. Therefore, the choice between one or the other database will depend on the type of application/company/use. For instance, for accounting applications, customer information, inventories, etc., the relational model is more suitable, as they require transactions involving multiple rows.

On the other hand, for content management systems, mobile apps, real-time analysis systems, databases with agile growth, or with a decentralized schema, NoSQL databases are the better option.

In summary:

When to use SQL?

  • If the data volume is not growing or is growing very slowly.
  • If the process requirements can be accommodated under a single server.
  • If we do not experience system usage peaks from users beyond what is anticipated.
  • Structured data.

When to use NoSQL?

  • If the data volume grows rapidly at specific times.
  • If the process needs cannot be anticipated.
  • If we experience frequent system usage peaks by the users.
  • Variable data.

Advantages and disadvantages of each system

The advantages of SQL point to greater support and a variety of tools since it has been in the market for a longer period. It is optimal for managing and retrieving data, allowing the addition of other SQL servers. On the contrary, its disadvantages lie in its lack of flexibility (objects need to be thoroughly validated before insertion). Furthermore, with increased database complexity, it requires more processing, influencing performance and resource consumption.

On the other hand, among the advantages of NoSQL databases is their high scalability, which helps reduce workload and is flexible with different types of data. However, disadvantages include compromised data integrity due to limited support, reduced query execution security, lack of standardization, and often, cases are incompatible with SQL databases.

At BIMEX, we want to emphasize that simply because it’s a “new” technology, one should not universally opt for NoSQL technologies. It is not a replacement but rather a different model offering distinct advantages in data usage, albeit carrying inconveniences in comparison to SQL.

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