AWS Certified Data Analytics Specialty 2021 – Hands-On - EMR, AWS integration, and Storage

The video tutorial explains how Amazon EMR integrates with various AWS services like EC2, VPC, S3, CloudWatch, IAM, CloudTrail, and Data Pipeline. It discusses storage options available in EMR, including HDFS, EMRFS, and local storage, highlighting their benefits and limitations. The tutorial emphasizes the importance of using EMRFS with S3 for persistent storage and addresses the issue of data consistency with S3. It also covers the use of local storage and EBS, noting their ephemeral nature and suitability for temporary data.

Which AWS service is used to configure permissions on EMR?

• Amazon S3

• AWS IAM

• Amazon CloudWatch

• AWS Lambda

What is a key characteristic of HDFS storage in EMR?

• It is stored in Amazon S3.

• It automatically scales with data size.

• It is ephemeral and data is lost after cluster termination.

• It is persistent after cluster termination.

What is the default block size in HDFS?

• 512 megabytes

• 256 megabytes

• 64 megabytes

• 128 megabytes

How does EMRFS improve data storage in EMR?

• By reducing the cost of storage

• By using local storage for faster access

• By allowing persistent storage in S3

• By increasing the block size in HDFS

What was a previous issue with using S3 for storage in EMR?

• Lack of redundancy

• Inconsistent data access

• High latency

• Limited storage capacity

What is a limitation of using the local file system for storage in EMR?

• It is ephemeral and not backed up.

• It is persistent after cluster termination.

• It automatically scales with data size.

• It is suitable for long-term data storage.

What happens if you manually detach an EBS volume while EMR is running?

• The volume will automatically reattach.

• EMR will treat it as a failure and replace it.

• The cluster will terminate.

• Data will be permanently lost.