AWS Certified Data Analytics Specialty 2021 - Hands-On! - Amazon Elastic MapReduce (EMR) Promises and Introduction to Ha

The video tutorial covers Amazon EMR pricing, cost management strategies, and handling node failures. It explains how to resize clusters and manage task nodes for temporary processing needs. The tutorial also discusses managed scaling in EMR, its evolution, and how it works with instance groups and fleets. Finally, it provides an introduction to Hadoop, detailing its architecture, components like HDFS and YARN, and the transition from MapReduce to Apache Spark for data processing.

What is a cost-effective way to manage EMR cluster expenses?

• Running clusters 24/7

• Using GPU instances

• Running tasks as a set of steps

• Increasing core nodes

What is the primary benefit of adding task nodes in EMR?

• Increasing storage capacity

• Handling temporary surges in processing needs

• Improving network speed

• Reducing data loss risk

What was the limitation of EMR automatic scaling before 2020?

• It was not based on CloudWatch metrics

• It could not mix different instance types

• It did not support Spark workloads

• It only supported instance fleets

In EMR managed scaling, which nodes are removed first when scaling down?

• On-demand instances

• Spot nodes

• Task nodes

• Core nodes

What is the role of Hadoop Common in the Hadoop architecture?

• It provides libraries and utilities for other modules

• It manages cluster resources

• It processes data in parallel

• It stores data blocks across instances

What is the primary function of YARN in Hadoop?

• To store data persistently

• To manage cluster resources centrally

• To process data using MapReduce

• To provide file system abstractions

Why is MapReduce called so?

• It reduces data storage requirements

• It maps data to key-value pairs and reduces them

• It maps network paths for data transfer

• It reduces processing time by half