Spark Programming in Python for Beginners with Apache Spark 3 - Introduction to Spark RDD API

The video tutorial introduces Spark RDDs, explaining their characteristics, such as resilience and fault tolerance. It covers how to create and process RDDs using Spark context and APIs, highlighting the differences between RDDs and DataFrames. The tutorial provides a detailed example of applying transformations and actions on RDDs, including schema application and data processing. It concludes by discussing the limitations of RDDs compared to DataFrames.

What does RDD stand for in Spark?

• Resilient Data Design

• Random Data Distribution

• Reliable Data Distribution

• Resilient Distributed Data

How do RDDs differ from DataFrames in terms of structure?

• RDDs require a predefined schema

• RDDs are always stored in a database

• RDDs are language-native objects without a schema

• RDDs have a row-column structure

How do RDDs handle fault tolerance?

• By using a backup server

• By storing information on how they are created

• By storing data in multiple locations

• By using a redundant network

What is the first step in creating an RDD using traditional Spark APIs?

• Create a DataFrame

• Create a Spark conf object

• Create a Spark context

• Create a Spark session

Which object is necessary to use RDD APIs in Spark?

• Spark session

• Spark context

• Spark executor

• Spark conf

What type of files can RDD APIs read directly?

• Excel and XML

• Binary and Sequence

• CSV and JSON

• Parquet and Avro

Which transformation is used to apply a function to each element of an RDD?

• Reduce

• GroupBy

• Filter

• Map

What is the purpose of the 'reduce by key' method in RDDs?

• To sort data

• To sum values by key

• To map data

• To filter data

What is a common challenge when using RDDs for data processing?

• Limited support for SQL operations

• Inability to handle large datasets

• Need for manual coding of operations

• Lack of flexibility

What is a key advantage of using DataFrame APIs over RDDs?

• DataFrames are faster to create

• DataFrames are more flexible

• DataFrames require less memory

• DataFrames offer SQL-like operations