What is the main purpose of a Databricks notebook?

The primary function of a Databricks notebook is interactive data exploration and coding. This environment allows users to write, run, and visualize code in real time, making it an effective tool for data analysts and data scientists. By supporting multiple programming languages, such as SQL, Python, R, and Scala, notebooks enable users to explore data dynamically, perform analysis, and create visualizations within the same interface.

Additionally, the notebook format allows for the integration of text, code, and output, providing a rich documentation aspect that enhances collaboration and sharing of insights among team members. As users interact with the data, they can iteratively refine their analyses, which leads to a more hands-on and engaging workflow.

The other options serve different purposes and do not encapsulate the primary use case of Databricks notebooks. For instance, while notebooks can be used in conjunction with large datasets, they are not primarily meant for storing that data. Instead, they are best utilized for analyses performed on data that may reside in various formats or storage options, such as Delta Lake or external databases. Static reports and web applications are functionalities that can be built with other tools or software, but they're not the central feature of Databricks notebooks, which emphasizes interactivity and real