Spark Programming in Python for Beginners with Apache Spark 3 - Spark DataFrameReader API

This video tutorial introduces the basics of using Spark Data Frame Reader API to read data from various sources. It covers the supported formats, options, and read modes, including permissive, drop malformed, and fail fast. The tutorial also explains schema inference and the importance of following standard practices over shortcuts for better code maintainability.

What is the primary method to access the Data Frame Reader in Spark?

• spark.read

• spark.load

• spark.data

• spark.frame

Which of the following is NOT a built-in format supported by Spark Data Frame Reader?

• JSON

• CSV

• XML

• JDBC

What is the default read mode in Spark when handling malformed records?

• Ignore

• Drop Malformed

• Permissive

• Fail Fast

In which read mode does Spark terminate immediately upon encountering a malformed record?

• Skip

• Fail Fast

• Drop Malformed

• Permissive

Why might you not need to specify a schema when using certain data sources?

• The data source is always structured

• The schema is inferred automatically

• The data source includes a predefined schema

• The schema is irrelevant

What is the advantage of following the standard style over using shortcuts in Spark?

• It increases execution speed

• It improves code maintainability

• It reduces code length

• It enhances data security

Which method is recommended to load data into a Data Frame after setting format, options, mode, and schema?

• load

• import

• fetch

• read