What is the simplest way to analyze data in a CSV file using Spark?

• 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. Read the data with Spark SQL

Answer: (B)

Loading the CSV into a DataFrame is the simplest starting point because DataFrames are Spark’s flexible and convenient way to work with structured data. When you read a CSV into a DataFrame, Spark automatically parses the rows and columns, and you can choose to infer the schema or provide it explicitly. With a DataFrame you gain immediate access to a rich set of operations for filtering, selecting columns, aggregating, joining, and more, all in a readable, chainable API.

From there, you can perform analysis directly with the DataFrame API or register a temporary view and run SQL queries if you prefer. This approach keeps things streamlined: you don’t have to move data to a warehouse, convert it to another format, or set up a separate SQL workflow to read the raw CSV.