AWS Certified Data Analytics Specialty 2021 – Hands-On - EMR Promises; Introduction to Hadoop

The video tutorial covers Amazon EMR pricing strategies, node management, and scaling techniques. It explains how to handle node failures, adjust capacity, and utilize managed scaling. The tutorial also provides an overview of Hadoop architecture, including HDFS, YARN, and MapReduce, highlighting their roles in data processing.

What is a cost-effective strategy for handling temporary increases in processing needs on an EMR cluster?

• Adding task nodes

• Using GPU instances

• Adding more core nodes

• Running the cluster 24/7

What is a potential risk when removing core nodes on the fly in an EMR cluster?

• Increased processing time

• Reduced network bandwidth

• Data loss

• Higher costs

What was the primary limitation of EMR automatic scaling before 2020?

• It could not scale down resources

• It was limited to instance groups

• It was not based on cloud metrics

• It only supported instance fleets

Which component of Hadoop is responsible for managing cluster resources?

• YARN

• Hadoop Common

• HDFS

• MapReduce

What is the primary function of HDFS in the Hadoop architecture?

• To manage cluster resources

• To store and replicate data blocks

• To process data in parallel

• To provide a user interface

What is the main advantage of using Apache Spark over MapReduce?

• It is easier to set up

• It is cheaper

• It is faster and more extensible

• It uses less memory

In the MapReduce framework, what is the role of the reducer function?

• To combine intermediate results and produce final output

• To manage cluster resources

• To map data to key-value pairs

• To store data in HDFS