PySpark and AWS: Master Big Data with PySpark and AWS - Why Spark

The video introduces Apache Spark, highlighting its speed, distributed processing, advanced analytics, real-time analysis, caching, fault tolerance, and ease of deployment. Spark is faster than competitors, supports distributed data processing, and offers advanced analytics through various libraries. It can handle real-time data streams and has a robust caching mechanism. Spark is fault-tolerant and easy to deploy across different programming languages. The video concludes with a preview of exploring Spark's ecosystem in the next session.

What is one of the main advantages of Spark's distributed behavior?

• It requires all data to be on a single machine.

• It allows processing across multiple machines.

• It only works with small datasets.

• It is slower than its competitors.

Which of the following is a library provided by Spark for advanced analytics?

• Spark SQL

• Spark HTML

• Spark XML

• Spark CSS

How does Spark handle real-time data processing?

• By processing data once a day.

• By configuring input streams for real-time data.

• By requiring all data upfront.

• By using batch processing only.

What feature of Spark helps in reducing computational overhead?

• Its caching mechanism.

• Its lack of fault tolerance.

• Its slow processing speed.

• Its requirement for single machine processing.

Which programming languages does Spark support for ease of deployment?

• C++, Ruby, Perl, and PHP

• Python, Scala, Java, and R

• Swift, Kotlin, Go, and Rust

• HTML, CSS, JavaScript, and SQL