Apache Spark 3 for Data Engineering and Analytics with Python - Reading a JSON File

The video tutorial introduces JSON, a lightweight data format, and its nested structure. It demonstrates creating headings in Markdown, importing data types, and defining a schema in PySpark. The tutorial guides viewers through reading a JSON file into a DataFrame, highlighting the use of multiline options and schema printing. The lesson concludes with a preview of upcoming topics on columns and expressions.

What does JSON stand for?

• Java Syntax Object Notation

• JavaScript Oriented Network

• JavaScript Online Notation

• JavaScript Object Notation

Which of the following is a characteristic of JSON files?

• They are only used for images

• They are not human-readable

• They are binary files

• They can represent nested data

What is an example of a nested field in a JSON file?

• An integer ID

• A boolean value

• A list of favorite movies

• A single string value

How do you create an H2 heading in Jupyter Lab using markdown?

• Using two hash symbols

• Using a single hash symbol

• Using three hash symbols

• Using four hash symbols

Which data type is used to represent a list of elements in PySpark?

• BooleanType

• StringType

• IntegerType

• ArrayType

What is the purpose of setting 'nullable' to true in a schema?

• To allow null values in the field

• To prevent null values in the field

• To convert all values to strings

• To ensure all values are unique

What is the first step in reading a JSON file into a DataFrame in PySpark?

• Printing the schema

• Creating a schema

• Downloading the JSON file

• Setting the file path

Which option is used to display the full content of a field in PySpark?

• Set truncate to false

• Set multiline to false

• Set truncate to true

• Set multiline to true

What does the 'multiline' option indicate when reading a JSON file?

• The file has data spread across multiple lines

• The file contains multiple JSON objects

• The file is a single line

• The file is encrypted

Why is it important to check the documentation for file options in Spark?

• To find available options for each file type

• To ensure compatibility with all file types

• To learn how to write JSON files

• To understand the syntax of Python