AWS Certified Data Analytics Specialty 2021 - Hands-On! - Kinesis Scaling

The video tutorial explains how to scale Amazon Kinesis by adding or splitting shards to increase stream capacity and merging shards to decrease it. It covers the importance of managing data ordering during resharding and introduces autoscaling, highlighting its limitations and the need for manual implementation. The tutorial emphasizes planning capacity in advance due to the time-consuming nature of resharding operations.

What is the primary purpose of shard splitting in Kinesis?

• To delete old data

• To merge multiple shards

• To increase the stream capacity

• To decrease the stream capacity

How does shard merging help in managing Kinesis streams?

• It duplicates the data across shards

• It increases the number of shards

• It speeds up data processing

• It decreases the stream capacity and saves costs

What can cause data to be read out of order after a resharding operation?

• Reading from child shards before the parent shard is fully read

• Splitting shards too quickly

• Using an incorrect partition key

• Merging shards with high traffic

What is the role of the Kinesis Client Library (KCL) in maintaining data order?

• It deletes expired data

• It splits shards based on traffic

• It ensures data is read in order after resharding

• It automatically merges shards

What is a key limitation of Kinesis scaling?

• It allows parallel resharding

• It requires manual intervention for every operation

• It cannot scale up or down too quickly

• It automatically adjusts to traffic changes

Which tool can be used to implement auto-scaling in Kinesis?

• AWS Lambda

• Kinesis Client Library

• Amazon S3

• AWS CloudFormation

How long does it take to double the number of shards from 1000 to 2000 in Kinesis?

• 24 hours

• 12 hours

• 8.3 hours

• 4 hours