The question "SQL or NoSQL?" appears in almost every job interview for data profiles in Argentina, and it's also one of the most important architecture decisions when you design a new system. The answer is not universal: it depends on the type of data you handle, the volume, the frequency of the queries, and the team that will maintain it.

In this guide we compare relational databases (PostgreSQL, MySQL) with non-relational databases (MongoDB, Redis, DynamoDB), we explain when it's best to use each one, and which to learn first if you're getting started in the world of data.

What are relational databases (SQL)

Relational databases organize information into tables with rows and columns, and use the SQL language (Structured Query Language) to query and manipulate the data. They have existed since the 70s and remain the standard in most enterprise systems.

The most popular ones are:

• PostgreSQL: open source, very robust, with support for advanced data types (JSON, arrays, geolocation). It's the preferred option of startups and tech companies in LATAM.

• MySQL: widely adopted in web applications, especially in LAMP stacks and e-commerce platforms. Very stable and with a large community.

• SQLite: embedded database, ideal for local development, prototypes, and mobile applications.

Key characteristics:

• Fixed structure: the data follows a predefined schema (tables with typed columns).

• ACID compliance: guarantees consistency, integrity, and durability of transactions.

• Relationships between tables: you can cross-reference data from different tables through foreign keys (JOIN).

• Ideal for structured data and complex queries.

What are non-relational databases (NoSQL)

NoSQL databases ("Not Only SQL") emerged to solve limitations of relational ones when the volume of data grows exponentially or when the structure of the data is flexible. They don't use tables or SQL as the main language.

There are several types of NoSQL:

• Document: they store data as JSON documents. The most popular is MongoDB.

• Key-value: like Redis, ideal for caching, sessions, and real-time counters.

• Columnar: like Apache Cassandra or DynamoDB, optimized for massive writes and distributed reads.

• Graph: like Neo4j, to model complex relationships between entities (social networks, recommendation systems).

SQL vs NoSQL: direct comparison

When to use SQL

Choose a relational database when:

• The data has clear relationships between entities (users, orders, products).

• You need strong consistency guarantees (financial transactions, inventory).

• The queries are complex and you need to cross-reference multiple tables.

• The team already knows SQL, which remains the most in-demand skill in Data job postings in Argentina.

• The data schema is relatively stable and doesn't change frequently.

When to use NoSQL

Choose a non-relational database when:

• The data doesn't have a fixed structure or changes frequently (product catalogs with variable attributes).

• You need to scale horizontally to handle millions of writes per second.

• You use real-time data: caching, rankings, user sessions (Redis is ideal for this).

• You store JSON documents like logs, analytics events, or responses from external APIs.

• You're building an application with a tree or graph data structure.

MongoDB in practice

MongoDB is the most adopted NoSQL database in Argentina for web applications. It stores data as BSON documents (similar to JSON), which makes it ideal for data that varies between records. For example, an e-commerce catalog where each product can have different attributes:

{
  "nombre": "Zapatilla Running Pro",
  "precio": 89.99,
  "talles": ["38", "39", "40", "41"],
  "color": "azul"
}
{
  "nombre": "Auricular Bluetooth",
  "precio": 49.99,
  "bateria_horas": 20,
  "conectividad": ["Bluetooth 5.0", "USB-C"]
}

{
  "nombre": "Zapatilla Running Pro",
  "precio": 89.99,
  "talles": ["38", "39", "40", "41"],
  "color": "azul"
}
{
  "nombre": "Auricular Bluetooth",
  "precio": 49.99,
  "bateria_horas": 20,
  "conectividad": ["Bluetooth 5.0", "USB-C"]
}

{
  "nombre": "Zapatilla Running Pro",
  "precio": 89.99,
  "talles": ["38", "39", "40", "41"],
  "color": "azul"
}
{
  "nombre": "Auricular Bluetooth",
  "precio": 49.99,
  "bateria_horas": 20,
  "conectividad": ["Bluetooth 5.0", "USB-C"]
}

{
  "nombre": "Zapatilla Running Pro",
  "precio": 89.99,
  "talles": ["38", "39", "40", "41"],
  "color": "azul"
}
{
  "nombre": "Auricular Bluetooth",
  "precio": 49.99,
  "bateria_horas": 20,
  "conectividad": ["Bluetooth 5.0", "USB-C"]
}

In SQL, modeling this catalog would require multiple tables and JOINs. In MongoDB, each document is independent and can have any structure, which greatly speeds up the initial development.

If you come from the world of spreadsheets and want to understand how to make the leap to SQL before exploring NoSQL, we recommend reading this SQL guide for analysts coming from Excel, which covers the transition step by step.

Redis: caching and real-time data

Redis is an in-memory key-value database, which makes it extremely fast (milliseconds of latency). It's used mainly for:

• Caching: storing results of expensive queries so as not to repeat them on every request.

• Sessions: storing authentication tokens and user session data.

• Message queues: handling asynchronous tasks between microservices.

• Real-time rankings: leaderboards in games or trading platforms.

Which one to learn first?

If you're entering the world of data, the answer is clear: learn SQL first. The reasons are practical:

• SQL is the most requested language in Data Analyst, Data Engineer, and Data Scientist job postings in Argentina.

• Most enterprise systems use relational databases.

• Relational thinking (tables, JOINs, aggregations) gives you a conceptual base that makes it easier to learn NoSQL later.

• Tools like BigQuery, Snowflake, and Redshift, which dominate data analysis in LATAM companies, use variants of SQL.

NoSQL comes later, once you already understand the fundamentals and need to solve a specific use case: scale, unstructured data, or real time. According to the Stack Overflow survey, PostgreSQL is the most used database by developers globally, followed by MySQL, which confirms that starting with SQL is the right decision.

Coderhouse courses to go deeper

If you want to learn to work with databases and position yourself for data or backend development roles in Argentina:

• Backend Development Career: covers SQL, NoSQL, and how to integrate them into real systems. Ideal for those who want to specialize on the server side.

• Full Stack Development Career: includes relational and non-relational databases as part of the complete stack of a modern developer.

• AI Engineering Course: for those who want to understand how AI systems handle large volumes of data, including vector databases like Pinecone or Weaviate.

Frequently asked questions

Can I use SQL and NoSQL in the same project?

Yes, it's a common practice. Many applications use PostgreSQL for transactional data (orders, users, payments) and Redis for caching or sessions. This architecture is called "polyglot persistence" and is standard in medium and large-scale systems in LATAM.

Is MongoDB better than PostgreSQL for all cases?

No. It depends on the use case. PostgreSQL is superior when you need complex JOINs, strict ACID transactions, and data consistency. MongoDB wins in schema flexibility, development speed, and handling of JSON documents. Many teams that started with MongoDB in startups migrate to PostgreSQL when their needs for consistency and complex queries grow.

Is SQL still relevant with the rise of AI?

More than ever. The data that feeds AI models needs to be stored, cleaned, and transformed, and SQL remains the main tool to do it. Platforms like BigQuery, Snowflake, and Databricks, which process the data of many AI systems, use variants of SQL. In addition, the rise of Text-to-SQL (natural language questions about databases) makes SQL even more accessible than before.

What type of projects is DynamoDB for?

DynamoDB, from AWS, is a serverless NoSQL database ideal for applications with very variable traffic, where you want to pay only for what you use. It's very popular in mobile applications, online games, and e-commerce with traffic spikes. Its biggest limitation is that complex queries are difficult without a very careful design of the schema from the start.

How long does it take to learn SQL from scratch?

With dedication of one to two hours a day, in four to six weeks you can handle the most frequent queries in work environments: SELECT with filters, JOINs between tables, GROUP BY, aggregation functions, and basic subqueries. For the level required in Data Analyst interviews in Argentina, a structured course of two to three months leaves you well positioned.