PySpark and AWS: Master Big Data with PySpark and AWS - Creating Spark DFs

The video tutorial explains the use of Spark with RDBMS, focusing on the creation and use of Spark contexts and sessions. It covers the differences between reading data with Spark context and Spark session, and how to handle Spark sessions in Databricks. The tutorial also demonstrates reading data from CSV and JSON files, and performing operations on data frames, highlighting the importance of specifying headers in data frames.

What is the primary difference between using Spark Context and Spark Session?

• Spark Session provides data as RDDs.

• Spark Context provides data as DataFrames.

• There is no difference between Spark Context and Spark Session.

• Spark Context provides data as RDDs, while Spark Session provides data as DataFrames.

What is the purpose of the 'getOrCreate' method in Spark?

• To create multiple Spark Sessions simultaneously.

• To delete an existing Spark Session.

• To create a new Spark Session if one does not exist.

• To create a new Spark Context.

What happens if you try to create a new Spark Session when one already exists in Databricks Community Edition?

• The new session is created and the old one is paused.

• An exception is raised.

• The existing session is terminated.

• A new session is created without any issues.

What is the convention for referring to a Spark Session in code?

• spark

• builder

• session

• context

Which method is used to read a CSV file using Spark Session?

• spark.read.json

• spark.read.text

• spark.read.csv

• spark.read.jdbc

Which of the following is NOT a format option available when reading data with Spark?

• JDBC

• CSV

• JSON

• XML

What is the default behavior of Spark when reading a file without specifying the format?

• It throws an error.

• It automatically detects the file format.

• It treats the file as a CSV.

• It treats the file as a JSON.

What is the role of the 'show' method in Spark DataFrames?

• To display the contents of the DataFrame.

• To save the DataFrame to a file.

• To transform the DataFrame into an RDD.

• To delete the DataFrame.

In Spark, what is the difference between transformations and actions?

• Transformations modify data, actions save data.

• There is no difference between transformations and actions.

• Transformations are immediate, actions are lazy.

• Transformations are lazy, actions trigger execution.

How can you specify that the first row of a CSV file should be treated as headers in a DataFrame?

• By using the 'header' option set to 'false'.

• By using the 'header' option set to 'true'.

• By using the 'format' option.

• By using the 'schema' option.