Apache Spark 3 for Data Engineering and Analytics with Python - Challenge Part 3 - Prepare 2019 Data

The video tutorial covers the process of reading 2019 data from a directory using modular programming. It emphasizes the importance of separating processes into modules for better maintenance. The tutorial guides viewers through renaming files, creating a new Python notebook, importing necessary libraries, and setting up a Spark session. It also demonstrates reading data from a directory and handling errors, concluding with a readiness to answer business questions.

What is the primary task discussed in the video?

• To generate business analytics

• To create a new Spark session

• To read 2019 data from Park files directory

• To analyze 2020 data from Park files directory

Why is modular programming recommended?

• It helps in combining all processes into one module

• It reduces the need for documentation

• It keeps the code organized and easier to maintain

• It makes the code run faster

What is the first step in creating a new Python notebook?

• Create a Spark session

• Import all necessary libraries

• Select Python 3

• Select Python 2

Which library is imported to create a Spark session?

• NumPy

• Pandas

• Matplotlib

• Pyspark.sql

What is the purpose of the 'spark.read' function?

• To write data to a file

• To read data from a directory

• To rename a file

• To create a new Spark session

What was the error encountered while reading the data?

• Missing slash in the path

• Incorrect data type

• Incorrect file path

• Missing semicolon

What is the final step after fixing the error in the data reading process?

• Create a new Spark session

• Print the data frame values

• Rename the data file

• Close the notebook