What type of data does a data lake primarily handle?

A data lake is primarily designed to handle unstructured data. This type of data includes information that does not fit neatly into traditional row-column formats found in relational databases. Examples of unstructured data include text files, images, audio files, and video content, which can vary widely in format and structure.

The essence of a data lake is its ability to store vast amounts of data in its raw form, without predefined schemas. This flexibility allows organizations to collect and analyze different types of data from diverse sources, enabling more comprehensive insights and analytics capabilities. As data lakes evolve, they can also accommodate semi-structured data, which may have some organizational properties (like JSON or XML files), but their primary strength lies in managing unstructured data.

In contrast, structured data refers to highly organized information that is easily searchable (like data in relational databases), while relational data is specifically associated with a relational database model characterized by defined relationships between tables. Both these types of data are not the primary focus of a data lake, as they require a more rigid structure to be effectively utilized.