What is the initial step to analyze data in a parquet file using Spark?

The initial step to analyze data in a parquet file using Spark involves loading the parquet file into a dataframe. Dataframes in Spark are distributed collections of data organized into columns, which allows for efficient processing and manipulation of structured data. When working with parquet files, which are a columnar storage format optimized for performance, the first action is to read the data into a dataframe. This allows users to utilize Spark’s powerful data processing and analysis capabilities, including the ability to perform transformations, aggregations, and queries on the data.

Loading the parquet file directly into a dataframe is advantageous as it maintains the schema and optimizes further operations like filtering and aggregating. This step sets the foundation for any subsequent analysis, ensuring that data can be manipulated effectively.

In contrast to the other options, converting the data to CSV or writing it to a SQL database adds unnecessary steps before analysis. Importing the data into a table in a serverless SQL pool would also not be the initial step, as it assumes that the data has already been loaded and processed into a usable format. Therefore, starting with loading the parquet file into a dataframe is the most logical and efficient action for data analysis with Spark.