To analyze data in a parquet file with Spark, what should you do?

Loading the parquet file into a dataframe is the correct approach for analyzing data in a parquet format using Spark. Parquet is a columnar storage file format optimized for use with big data processing frameworks like Apache Spark. Dataframes in Spark are the primary data structure used for handling structured data. By loading a parquet file into a dataframe, you can take advantage of Spark's distributed computing capabilities to perform complex transformations, aggregations, and analytical queries efficiently.

This method maintains the columnar storage benefits of parquet, such as improved performance and reduced I/O compared to row-based storage formats. Additionally, working with dataframes allows you to leverage Spark's rich API for data manipulation and analysis.

The other options, while they present alternatives, do not directly facilitate the analysis of data in its original parquet format using Spark. Importing data into a table in a serverless SQL pool or exporting it to a SQL database could lead to additional overhead, and converting the data to CSV format would negate the advantages of using parquet, such as compression and efficient querying. Thus, the most effective way to work with parquet files in Spark is to load them into a dataframe.