AWS Certified Data Analytics Specialty 2021 – Hands-On - Introduction to Apache Spark

The video tutorial provides an in-depth look at Apache Spark, a distributed processing framework for big data. It highlights Spark's advantages over MapReduce, such as in-memory caching and query optimization. The tutorial covers Spark's programming languages, code reusability, and its applications in analytics and machine learning. It explains Spark's architecture, including the driver program and executors, and details core components like Spark SQL, Streaming, and Mllib. The video concludes with a practical example of Structured Streaming, demonstrating real-time data processing with minimal code.

What is one of the main reasons Spark outperforms MapReduce?

• It relies on network-based processing.

• It has a query execution optimizer.

• It uses disk storage for operations.

• It is written in Java.

Which languages are most commonly used to write Spark applications?

• Java and R

• Python and Scala

• Ruby and Python

• C++ and Java

What type of applications is Spark best suited for?

• OLTP applications

• Real-time transaction processing

• OLAP applications

• Web hosting

What is the role of the Spark driver program?

• To manage memory and fault recovery

• To coordinate processes across the cluster

• To execute SQL queries

• To store data associated with jobs

Which component of Spark is responsible for machine learning capabilities?

• Mllib

• Spark Streaming

• Spark SQL

• GraphX

What is a key feature of Structured Streaming in Spark?

• It treats data as an unbounded table.

• It only supports batch processing.

• It uses a map and reduce paradigm.

• It processes data in mini-batches.

In the example of structured streaming, what is the data source being monitored?

• HDFS

• S3 bucket

• Kafka

• MySQL database