Big data is the name given to very large data sets that arrive fast or come in varied formats. They are so heavy that a spreadsheet or a common database cannot process them well.

The term became famous for being linked to three ideas: volume, velocity and variety.

How it works

When data does not fit on a single machine, it is split and stored across multiple computers. Processing is also split: each machine analyzes a piece, and then the results are combined.

The flow usually has these steps:

  • Collection of data from websites, applications, sensors and systems.
  • Storage in structures like data lake or data warehouse.
  • Cleaning and organization, often by data pipelines.
  • Analysis, reporting and training of machine learning models.

Practical example

An online store receives thousands of clicks, purchases and searches per minute. Each click becomes a record. In one month, there are millions of rows.

With big data, the team cross references these records to find out which products are bought together, at what time sales increase and which customers tend to cancel. With this view, it adjusts offers and inventory.

SQL
SELECT produto, COUNT(*) AS vendas
FROM pedidos
WHERE data >= '2026-01-01'
GROUP BY produto
ORDER BY vendas DESC
LIMIT 10;

This query shows the ten best selling products. In big data, the principle is the same, but running over much larger volumes.

What it is for

Big data is used to make decisions based on evidence. It appears in product recommendation, fraud detection, demand forecasting, health and logistics.

For AI, data is fuel. Models learn from examples, and the more relevant and well organized the data is, the better the result tends to be. That is why big data and machine learning go together.

Caveats

More data does not mean better answers. Bad, duplicated or biased data generates bad conclusions. It is also necessary to take care of privacy and follow laws like LGPD when dealing with people's information.

Difference between big data and data science

Big data deals with volume and infrastructure to handle data. Data science is the practice of extracting knowledge from them, using statistics and models. One complements the other.