How can data be imported from object storage using Databricks SQL?

The correct approach to import data from object storage using Databricks SQL is to create an external table that references the data in the object storage. This method allows you to define a table structure that points directly to the files stored in an external location, such as AWS S3, Azure Blob Storage, or Google Cloud Storage. By using an external table, you can efficiently query and analyze the data without having to load it into Databricks, which saves time and resources.

When you define an external table, you specify the location of the data files, along with the schema (columns and data types) of the data. Databricks SQL can then read this structured metadata and perform operations directly on the data stored in the object store.

This mechanism is particularly useful for scenarios where data is large and frequently changes, as it enables users to keep the data up-to-date without needing to physically move or duplicate the data in Databricks itself. This way, your queries can directly access and analyze the latest data available in the object storage.

Other options are not efficient methods for importing data into Databricks SQL. For instance, downloading files directly would require manual intervention, and using APIs to fetch data might add unnecessary complexity. Compressing the data into