PySpark and AWS: Master Big Data with PySpark and AWS - Applications of PySpark

The video tutorial covers various applications of PySpark, focusing on its use in streaming data, machine learning, batch data analysis, ETL processes, and data replication. PySpark's ability to handle real-time data, perform advanced analytics, and support data migration and replication is highlighted. The tutorial aims to provide an understanding of these key applications, acknowledging that not all PySpark applications can be covered in a single video.

What is a key feature of Spark Streaming?

• It does not support real-time analytics.

• It can process streaming data in real-time.

• It is limited to small data sets.

• It can only process batch data.

Which of the following is a component of Spark's machine learning library?

• NoSQL Database

• Hadoop

• MLlib

• SQL Database

What type of data analysis does Spark support besides real-time analysis?

• Only streaming data analysis

• Batch data analysis

• Only machine learning

• Only ETL processes

What does ETL stand for in the context of Spark?

• Edit, Train, Learn

• Execute, Transfer, Link

• Evaluate, Test, Launch

• Extract, Transform, Load

How does Spark handle data replication?

• By replicating the entire data set every time

• By ignoring changes in the source

• By using SQL databases only

• By replicating only the changes in the source