How do you read data into a DataFrame in Databricks?

Reading data into a DataFrame in Databricks can be effectively accomplished using built-in functions, such as spark.read.csv(). This method is part of the Spark API and is designed to facilitate the loading of various data formats into a DataFrame, which is the primary data structure used in Apache Spark. By using these built-in functions, users can conveniently specify the format of the data they are working with, the path to the data files, and other options like header presence, delimiter, or schema.

This approach is not only efficient but also allows for handling larger datasets seamlessly while leveraging Spark’s optimizations. The versatility of the spark.read API extends beyond CSV files; it includes support for JSON, Parquet, Avro, and more, enabling users to work with different data sources in a consistent manner.

Other methods such as running SQL queries, manually entering data, or using external scripts are less practical for ingesting structured data directly into a DataFrame. SQL queries can be useful for manipulating data that is already in a DataFrame but are not specifically designed for the initial reading or loading of data. Manually entering data is feasible for simple or small datasets but is not scalable and often impractical for larger datasets. Using external scripts