Apache Spark 3 for Data Engineering and Analytics with Python - Structured APIs Introduction

The video tutorial discusses the motivation behind the introduction of structured APIs in Spark, highlighting both business and technical reasons. It compares the RDF API with SQL, emphasizing the ease of use and readability of SQL. The tutorial explains the difference between typed and untyped objects, focusing on the advantages of using Scala and Java for data integrity. It also introduces the DataFrame API, showcasing its features and benefits, such as easy-to-read code and built-in data manipulation functions.

What was one of the main business reasons for introducing structured APIs in Spark?

• To make Spark easier to learn and adopt

• To eliminate the need for SQL

• To align with the RDF API

• To make Spark more complex

Why might someone prefer using SQL over Spark's RDF API?

• SQL is less efficient

• SQL is easier to read and understand

• SQL is more cryptic

• SQL requires more code

What is a key feature of the Dataset API in Spark?

• It supports dynamic typing

• It is available in Python

• It is untyped

• It uses strongly typed JVM objects

What is the main difference between typed and untyped objects in programming?

• Typed objects enforce a data type

• Untyped objects enforce a data type

• Typed objects can change types

• Untyped objects are only in Scala

Which language allows dynamic typing, making it more flexible but potentially less safe?

• C++

• Python

• Scala

• Java

What advantage does the DataFrame API offer over the RDF API?

• It provides easy-to-read code and schemas

• It is only available in Java

• It is more difficult to read

• It lacks built-in functions

What additional feature does the DataFrame API bring to data manipulation?

• It complicates the code

• It only works with untyped data

• It introduces built-in data manipulation functions

• It removes the need for schemas