Scala & Spark-Master Big Data with Scala and Spark - Spark DF Read Data

This video tutorial explains the difference between Spark context and Spark session, emphasizing the use of Spark sessions for working with DataFrames. It provides a step-by-step guide on creating a Spark session, including setting the app name and using 'get or create'. The tutorial also covers reading data using Spark DataFrames, highlighting the importance of specifying options like headers. The video concludes with a brief summary and a preview of the next video, which will explore DataFrames in more detail.

What is the main difference between using Spark context and Spark session?

• Both read data as DataFrames.

• Both read data as RDDs.

• Spark context reads data as RDDs, while Spark session reads as DataFrames.

• Spark context reads data as DataFrames, while Spark session reads as RDDs.

What is the first step in creating a Spark session?

• Reading the data file

• Setting the application name

• Creating a Spark context

• Importing org.apache.spark.sql.SparkSession

What method is used to create or get a Spark session?

• createSession

• getOrCreate

• getSession

• createOrGet

What is the purpose of specifying the application name when creating a Spark session?

• To specify the data source

• To determine the number of executors

• To identify the Spark session in logs

• To set the default data format

Which method is used to display data in a DataFrame?

• show

• display

• collect

• print

What option should be set to true to treat the first row as headers in a CSV file?

• firstRowHeader

• setHeader

• header

• useHeader

What is a key advantage of using Spark DataFrames over RDDs?

• DataFrames offer more options for data reading.

• RDDs are faster than DataFrames.

• RDDs provide more options for data manipulation.

• DataFrames are more complex to use.