What is the simplest way to analyze data stored as CSV using Spark in Fabric?

• A. Import the data into a table in a warehouse.
• B. Load the file into a dataframe.
• C. Convert the data to Parquet format.
• D. Use Spark SQL to register a temporary view.

Answer: (B)

The main idea is that Spark’s DataFrame provides the most straightforward and powerful entry point for analyzing structured data like CSV. Loading the CSV into a DataFrame gives you a tabular, column-named representation with optional schema inference, so you can immediately perform filters, aggregations, joins, and transformations using concise, expressive APIs. In Fabric, you’d typically read the CSV with spark.read.csv (with header and, if desired, inferSchema options), and you gain immediate access to the data for analysis. If you later want to use SQL, you can register a temporary view from that DataFrame, but the essential first step is bringing the data into a DataFrame.

Other approaches add extra steps or constraints. Importing into a warehouse means moving the data into a separate storage system with its own table and schema management, which is heavier for quick analysis. Converting to Parquet changes the data format and requires a conversion step before you can analyze it, adding overhead. Using Spark SQL to register a temporary view is a useful technique, but it’s an optional layer on top of having the data in a DataFrame, not the simplest starting point for analysis.