PySpark and AWS: Master Big Data with PySpark and AWS - Spark Streaming DF

This video tutorial covers setting up a Spark environment, creating a Spark session, and using read streams to process data. It explains how to handle existing files in Spark streaming and demonstrates writing stream outputs to the console. The tutorial also discusses visualizing data in Databricks and local environments, emphasizing the importance of directory management and output modes.

What is the purpose of running a command to remove files in the directory before starting with Spark?

• To free up disk space

• To ensure no old data interferes with new operations

• To speed up the Spark session

• To create a backup of the files

Why is 'getOrCreate' used when creating a Spark session?

• To automatically configure the session settings

• To ensure the session is created in a specific directory

• To avoid exceptions by reusing an existing session if available

• To create multiple sessions simultaneously

What is the main difference between 'read' and 'readStream' in Spark?

• Read is for batch processing, while readStream is for streaming data

• Read is faster than readStream

• ReadStream can only handle text files

• Read requires more memory than readStream

How does Spark Streaming handle files that were already in the directory before the session started?

• It archives old files for later processing

• It deletes old files before processing

• It processes all files, old and new

• It ignores old files and only processes new ones

What is the role of the 'complete' output mode in Spark Streaming?

• To display only the new data

• To show the entire output, not just updates

• To save the output to a file

• To visualize the data in a graph

In which environment is it easiest to visualize Spark Streaming data?

• Standalone server

• Local machine

• Cloud-based cluster

• Databricks environment

What is a recommended way to observe Spark Streaming data if not using Databricks?

• Use a third-party visualization tool

• Use a local database to store the data

• Write the data to a file and observe it

• Print the data to the console