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 covers Spark's advantages over MapReduce, its programming languages, and code reusability. The tutorial also explores Spark's real-time analytics, machine learning capabilities, and architecture, including its core components and additional systems like Spark SQL and Streaming. It highlights the use of MLlib for machine learning and GraphX for graph processing. Finally, it delves into structured streaming for real-time data processing.

What is one of the main reasons Spark outperforms MapReduce?

• It relies on network-based processing.

• It is written in Java.

• It has a query execution optimizer.

• It uses disk storage for operations.

Which component of Spark is responsible for coordinating processes across the cluster?

• Spark SQL

• Spark Core

• Driver Program

• Executor

Which of the following is NOT a capability of Spark's MLlib?

• Collaborative filtering

• Graph processing

• Clustering

• Regression

What is the primary function of Spark SQL?

• To execute machine learning algorithms

• To manage memory and fault recovery

• To provide a SQL-style interface for data querying

• To perform graph processing

In Spark, what is a dataset equivalent to in Python?

• A list

• A dictionary

• A data frame

• A tuple

What is a key feature of Spark Streaming?

• Real-time data processing

• Requires a standalone cluster

• Limited to OLTP applications

• Batch processing only

What does structured streaming in Spark allow you to do?

• Process data in fixed batches

• Continuously process data as it arrives

• Perform OLTP transactions

• Execute only SQL queries