What are Interactive clusters used for in Databricks?

Interactive clusters in Databricks are primarily designed for performing ad-hoc analytics and development tasks. These clusters enable users to quickly spin up a computing environment where they can execute interactive commands, run notebooks, and perform exploratory data analysis. The ability to interactively run code, visualize data, and manipulate datasets in real-time is a central feature of these clusters, which caters to data scientists and analysts who require flexibility and immediacy in their workflows.

Unlike batch processing clusters, which are optimized for running scheduled jobs on large datasets, interactive clusters provide the resources necessary for real-time feedback and iterative development. This interactive nature allows for a more responsive experience when conducting ad-hoc queries, testing algorithms, or exploring data visualization options.

The other options relate to different functionalities not specifically associated with interactive clusters. For example, batch processing jobs and managing backups or archives have distinct use cases that are better served by other types of clusters or services within the Databricks environment. Storing large volumes of data securely typically involves data storage solutions rather than the interactive processing capabilities of interactive clusters.