What role do DataFrames play in Databricks?

DataFrames in Databricks serve as a powerful and flexible data structure designed for handling large datasets. They are essentially distributed collections of data structured in named columns, which makes them particularly suitable for data manipulation and analysis. This organization allows users to perform operations such as filtering, aggregating, and transforming data in an efficient manner, leveraging Spark's distributed computing capabilities.

The use of named columns enhances readability and usability, as users can access data similarly to how they would in a relational database table. DataFrames enable integration with various data sources, allowing for easy reading and writing of diverse data formats, including CSV, JSON, Parquet, and more.

This structure is foundational in workflows that involve data science and machine learning within Databricks, as it allows for seamless transitions between data preparation, modeling, and evaluation processes using familiar APIs.

In contrast, options that suggest DataFrames are graphical user interfaces, serve as storage units for raw data, or are limited solely to numerical data processing do not accurately capture the core functionalities and versatility of DataFrames in the context of Databricks and distributed data analytics.