Scala & Spark-Master Big Data with Scala and Spark - Hadoop Ecosystem

The video explores the Hadoop ecosystem, focusing on its core components: HDFS, YARN, and MapReduce. It explains how these components empower Apache Spark, a powerful data processing tool. HDFS allows data distribution across multiple storage devices, YARN manages resources like an operating system, and MapReduce provides a framework for data computation. Spark enhances these capabilities by optimizing data processing, making it significantly faster than traditional MapReduce frameworks. The video aims to build an understanding of these technologies and their interconnections.

What is one of the most prominent features of Spark that attracts engineers and data scientists?

• Its use of traditional databases

• Its reliance on a single machine for data storage

• Its distributed processing capability

• Its ability to run on a single CPU

Which of the following is NOT a core component of the Hadoop ecosystem?

• HDFS

• YARN

• MapReduce

• SQL

What does HDFS stand for?

• Hadoop Distributed Flow Storage

• Hadoop Data Flow System

• Hadoop Data File Storage

• Hadoop Distributed File System

How does YARN function within the Hadoop ecosystem?

• As a data storage system

• As a network protocol

• As a resource manager

• As a data processing tool

Which component of Hadoop is considered its operating system?

• MapReduce

• YARN

• HDFS

• Spark

What is the primary advantage of Spark over MapReduce?

• Spark is slower but more reliable

• Spark requires more resources

• Spark is less efficient

• Spark is 10 to 100 times faster

What is the role of MapReduce in the Hadoop ecosystem?

• To manage resources

• To distribute and compute data

• To provide user interfaces

• To store data